Understanding the carbon (C) recovery potential of secondary forests (SF) is essential for reducing uncertainties regarding the contribution of Amazon forests to the global carbon budget, and for strengthening national and sub-national climate mitigation policies aligned with Brazil’s commitments under the Paris Agreement. We, hence, focus on Pará state in eastern Amazon to evaluate how regional edaphoclimatic conditions, prior land-use duration, and fire recurrence influenced C removal rates of SFs. We combined Earth observation-based aboveground biomass and SF age data, derived from multitemporal land cover maps, to construct chronosequences representing four past land-use classes: undisturbed SF (regrowth immediately after deforestation), anthropogenic use with fire, anthropogenic use only, and fire only across three edaphoclimatic regions. We characterised spatiotemporal variations in C gain and projected potential removals by 2035 under three protection scenarios (SF age ≥ 1, ≥5, and ≥ 10 years). We showed that a history of extensive land use and fire before forest regeneration reduced C gain rates by 27
Carbon storage, a key indicator of terrestrial sequestration, reflects land use impacts on ecological security and is fundamental to regional carbon management and climate change mitigation. To support basin-scale carbon management strategies, this study examines the middle Yangtze River urban agglomeration. We coupled PLUS and InVEST models to analyze historical (2000–2020) and simulate 2030 land use and carbon storage under three scenarios: Natural Development (NS), Ecological Protection (ES), and Urban Development (DS). Using a 1 km shoreline buffer grid and Getis-Ord Gi*, we identified carbon storage hotspot/coldspot changes. Multi-temporal raster analysis first revealed three key waterfront functional units: Stable Core, Continuously Degrading, and Policy Response Areas. Results showed: (1) During 2000–2020, built-up land expanded 61.79
Urban agglomerations are key areas for carbon emissions, and lake ecosystems are statistically associated with the regulation of their carbon cycles. However, existing research on carbon emissions in urban agglomerations largely focuses on macro-scale studies. It lacks a systematic analysis of the multidimensional factors associated with carbon emissions at the fine community scale in lake-type urban agglomerations, particularly neglecting watershed-specific ecological processes that are associated with carbon regulation such as aquatic vegetation. This study uses the Nansi Lake Basin urban agglomeration as its research site, aiming to quantify the multidimensional factors associated with community-scale carbon emissions and simulate spatially gradient-based carbon reduction optimization strategies. Based on multi-source data from 2014 to 2022, XGBoost was used for regression prediction and feature importance analysis, combined with scenario design and optimal strategy simulation. The results show that multicollinearity and weak linear correlation among independent variables, but the ensemble model has high prediction accuracy (average R² = 0.866). Feature importance analysis indicates that carbon emission factors exhibit a dual characteristic of “socio-economic dominance” and “prominent watershed characteristics.” This study designed three-tiered intervention scenarios: key intervention, moderate intervention, and comprehensive optimization. Based on the model’s predictions, adjusting policy-adjustable indicators such as Temperature and Population is associated with carbon emission reductions of approximately 7,000 tC. DEM is treated only as a static topographic control for model sensitivity diagnosis and is not considered a policy-adjustable mitigation option. This research provides a quantifiable and spatially explicit framework for exploring carbon reduction options for the Nansi Lake Basin and similar lake-type urban clusters and has significant decision-making reference value for formulating differentiated regional low-carbon development plans.
Background Grassland soils on the Qinghai–Xizang Plateau (QXP) represent an important terrestrial carbon reservoir and are affected by grazing, nitrogen (N) deposition, and climate variability. However, the interactive effects of grazing and N addition on soil organic carbon (SOC) stocks across soil depths remain poorly understood. We conducted a three-year factorial field experiment in an alpine meadow on the QXP to examine the effects of three stocking rates and four N-addition rates on plant biomass, plant functional composition, and SOC stocks at 0–30 cm depth. Climatic variables were obtained from an on-site weather station and were treated as observational interannual covariates rather than experimental factors. Results During the experimental period, a stocking rate of 8 sheep ha–1 was associated with higher SOC stocks at selected soil depths and sampling years. N addition mitigated some negative effects of grazing on plant aboveground biomass (AGB), but had negligible net effects on SOC stocks. Grazing weakened negative associations between interannual climatic covariates and SOC stocks. The contributions of grazing, climatic covariates, plant functional composition, and soil properties to SOC stocks varied with soil depth. Plant functional composition, particularly the AGB ratio of traditional herbage (TH) to functional native herbage (FNH), made an important contribution to SOC stocks in the upper soil layers, whereas the contributions of climatic covariates and soil properties increased with depth. Conclusions Stocking rates at 8 sheep ha–1 were compatible with higher SOC stocks under the conditions of this three-year experiment, but long-term and multi-site studies are needed to evaluate their broader applicability.
Soil erosion and deposition reshape carbon pools, but how geomorphic redistribution regulates microbial communities and CO2 flux remain unclear. In tropical island watersheds, intense rainfall, steep terrain, and coarse-textured soils may intensify soil redistribution and amplify variability in surface carbon dynamics. This study evaluated how erosion-deposition patterns were associated with soil physicochemical conditions, microbial assemblages, and soil CO2 flux along four transects within the Tengqiao River watershed, Baoting, Hainan Island, China. The Unit Stream Power-based Erosion/Deposition (USPED) model revealed spatial heterogeneity in erosion and deposition rates, ranging from − 7.90 to 22.61 t ha− 1 yr− 1 across the 20 sampling sites. Compared with erosion sites, deposition sites tended to show higher nutrient contents, SOC concentration and surface SOC stock, together with lower bulk density (BD). Microbial community composition and co-occurrence networks also differed between erosion and deposition sites. Deposition sites favored taxa linked to nutrient cycling, whereas erosion sites were associated with stress-tolerant taxa. Across the measured months, soil CO2 fluxes were significantly higher in the rainy season than in the dry season. Partial least squares structural equation modeling (PLS-SEM) showed that erosion-deposition dynamics reduced soil water content (SWC) and pH, increased BD, and significantly reduced SOC. Soil physical condition (SWC, BD) and pH were further associated with significant decreases in bacterial and fungal diversity, and bacterial diversity showed a significant positive association with soil CO2 flux. Erosion-deposition processes were associated with surface soil redistribution, microbial community restructuring, and seasonal variation in soil CO2 flux. In this coarse-textured tropical island watershed, erosion-deposition dynamics reduced SOC and altered soil physical and chemical conditions, thereby weakening bacterial and fungal diversity. The significant linkage between bacterial diversity and soil CO2 flux suggests that bacterial assemblages may play an important role in mediating short-term carbon release. These results highlight the need to integrate geomorphic and microbial processes when assessing carbon responses in erosion-prone ecosystems.
