The spatiotemporal variability of precipitation profoundly influences terrestrial carbon fluxes, driving shifts between carbon source and sink dynamics through gross primary productivity (GPP) and ecosystem respiration (ER). As a result, the sensitivities of GPP and ER to precipitation (S_GPP and S_ER), along with their differential responses, are pivotal for understanding ecosystem reactions to precipitation changes and predicting future ecosystem functions. However, comprehensive evaluations of the spatiotemporal variability and differences in S_GPP and S_ER remain notably scarce. In this study, we utilized eddy covariance flux data to investigate the spatial patterns, temporal dynamics, and differences in S_GPP and S_ER. Spatially, S_GPP and S_ER were generally strongly correlated. Among different ecosystems, the correlation between S_GPP and S_ER was lowest in mixed forest and highest in broadleaf and needleleaf forest. Within the same ecosystem, S_GPP and S_ER exhibited considerable variation but showed no significant differences. In contrast, they differed significantly across ecosystems, with pronounced variability in their magnitudes. For example, shrubland exhibited the highest values for S_GPP, whereas needleleaf forest showed the highest values for S_ER. Temporally, S_ER demonstrated more pronounced changes than S_GPP. Different ecosystems displayed distinct trends: shrubland exhibited an upward trend for both metrics, while grassland showed a downward trend in both S_GPP and S_ER. Forest, on the other hand, maintained stable S_GPP but displayed a downward trend in S_ER. Additionally, S_GPP and S_ER exhibited a notable non-linear response to changes in the aridity index (AI), with both showing a rapid decline followed by stabilization. However, S_ER demonstrated a wider adaptive range to precipitation changes. Generally, this research enhances our understanding of the spatiotemporal variations in ecosystem carbon fluxes under changing precipitation patterns.

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach.

Leaf functional traits are widely used to describe interspecific variation in plant resource-use strategies and photosynthetic performance. We examined leaf traits and photosynthetic characteristics in 21 common woody species (11 tree and 10 shrub species; two individuals per species) sampled from subtropical secondary-forest vegetation in Chongqing, southwestern China. Maximum net photosynthetic rate (Pnmax), apparent quantum yield (AQY), light saturation point (LSP), and light compensation point (LCP) were derived from species-level fitted light-response curves. Among the 17 species with accepted fits, Pnmax ranged from 3.35 to 12.93 μmol m⁻² s⁻¹. Estimates for Jacaranda mimosifolia and Vitex negundo var. cannabifolia were extrapolated beyond the measured irradiance range (14.66 and 16.23 μmol m⁻² s⁻¹, respectively). No finite Pnmax was defined for Osmanthus fragrans or Rhododendron simsii because β was estimated at zero; their fitted asymptotic net photosynthetic rates were 3.33 and 4.29 μmol m⁻² s⁻¹, respectively. Nitrogen content per unit leaf area (Narea) ranged from 0.46 to 2.28 g m⁻². Trait analyses used species means as independent observations. Spearman correlations were evaluated with Benjamini–Hochberg false discovery rate correction, and prespecified candidate regression models were compared using adjusted R², AICc, and leave-one-species-out cross-validation. After FDR correction, Narea was positively associated with Pnmax (Spearman’s ρ = 0.673, q = 0.0137) and AQY (ρ = 0.666, q = 0.0137). No statistically significant differences in photosynthetic parameters remained between trees and shrubs after correction for multiple testing. Among the prespecified candidate models, the Narea-only model had the lowest AICc for Pnmax (adjusted R² = 0.440). For AQY, the Narea-only and Narea + LA models had similar support (ΔAICc = 0.41), and the data did not clearly distinguish between them. Adding LA did not significantly improve either model in the incremental F-tests (p = 0.486 for Pnmax and p = 0.135 for AQY). The positive Pnmax–Narea association remained after controlling for LMA using partial Spearman correlation (partial ρ = 0.762, p < 0.001), indicating that shared LMA normalization alone did not explain the association in this dataset. These species-level associations are observational and do not establish causation.