Yunqi Wang

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.

The remote sensing ecological index (RSEI) is widely used to track regional ecological quality, and a growing literature revises it by substituting a satellite canopy height term for its greenness component. Such revisions are judged by how far the modified index departs from the original, a criterion that cannot work. For any index defined as the first principal component of k standardised components, replacing one component while retaining the rest leaves the two indices correlated above a floor fixed by the construction, even when the incoming component is orthogonal to everything already in the index. With retained components correlated at 0.4, the floor runs from 0.959 to 0.988 at k = 4 and reaches 0.990 by k = 8, so resolution falls away as indicators grow. We derive the bound analytically, then supply the validation it cannot provide, over 12,499,740 forest pixels at 30 m in the Three Gorges Reservoir Area, where growing-season NDVI varies by only 4.98% of its mean. A canopy height layer trained on GEDI RH98 scored R² = 0.158 on 350 locked footprints, against 0.024 for the published product. Three-dimensional greenness built from it raised the correlation of the index with measured canopy height from 0.176 to 0.225 (bootstrap 95% CI of the gain [0.0144, 0.0844]), although both explain under 6% of its variance. The two indices meanwhile correlated at 0.9520 against a floor of 0.9490. Revised indicators need validation against data lying outside the index.

Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has been inadequate, especially in terms of long-term monitoring and mapping. Moreover, the sediment reduction effect of terrace construction is not yet fully understood. Therefore, this study utilizes Landsat series data, integrating remote sensing imaging principles with machine learning techniques to achieve long–term temporal sequence mapping of terraces at a 30 m spatial resolution on the Loess Plateau. The sediment reduction effect brought about by terrace construction on the Loess Plateau is quantified using a sediment reduction formula. The results show that Elevation (Ele.), red band (R), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Reflectance of Vegetation (NIRv) are key parameters for remote sensing identification of terraces. These five remote sensing variables explain 88% of the terrace recognition variance. Coupling the Random Forest classification model with the LandTrendr algorithm allows for rapid time-series mapping of terrace spatial distribution characteristics on the Loess Plateau. The producer’s accuracy of terrace identification is 93.49%, the user’s accuracy is 93.81%, the overall accuracy is 88.61%, and the Kappa coefficient is 0.87. The LandTrendr algorithm effectively removes terraces affected by human activities. Terraces are mainly distributed in the southeastern Loess areas, including provinces such as Gansu, Shaanxi, and Ningxia. Over the past 30 years, the terrace area on the Loess Plateau has increased from 0.9790 million hectares in 1990 to 9.8981 million hectares in 2020. The sediment reduction effect is particularly notable, with an average reduction of 49.75% in soil erosion across the region. This indicates that terraces are a key measure for soil erosion control in the region and a critical strategy for improving farmland productivity. The data from this study provides scientific evidence for soil erosion control on the Loess Plateau and enhances the precision of terrace management.

Moso bamboo (Phyllostachys pubescens) is one of the most common species of bamboo in East Asia, and plays a crucial role in regulating hydrological and biogeochemical processes in forest ecosystems. However, throughfall variability and its time stability in Moso bamboo forests remain unclear. Here, we investigated the spatial variability and temporal stability of throughfall in a Moso bamboo forest in China, and the effects of rainfall characteristics and leaf area index (LAI) on the variability of throughfall, and tree locations on the temporal stability of throughfall were systematically evaluated. The results show that throughfall occupied 74.3% of rainfall in the forest. The coefficient of variation of throughfall (throughfall CV) for rainfall events and throughfall collectors were 18.1% and 19.5%, respectively, and the spatial autocorrelation of the throughfall CV was not significant according to the global Moran’s I. Throughfall CV had a significantly negative correlation with rainfall amount and rainfall intensity, whereas it increased with the increase in LAI. The temporal stability plot indicated that the extreme wet and dry persistence were highly stable. We also found that normalized throughfall increased with the increase in distance from the nearest tree trunk. Our findings are expected to assist in the accurate assessment of throughfall and soil water within bamboo forests.

Under the global climate governance framework, the Paris Agreement and the China–U.S. Glasgow Joint Declaration established a non-negotiable target of limiting 21st-century temperature rise to 1.5 °C. To date, over 130 nations have pledged carbon neutrality by mid-century, with agricultural activities contributing 25% of global greenhouse gas (GHG) emissions. The spatiotemporal dynamics of these emissions critically determine the operational efficacy of carbon peaking and neutrality strategies. While China’s Nationally Determined Contributions (NDCs) commit to achieving carbon peaking by 2030, a policy gap persists regarding differentiated implementation pathways at the county level. Addressing this challenge, this study selects the Three Gorges Reservoir (TGRA)—a region characterized by monocultural cropping systems and intensive fertilizer dependency—as a representative case. Guided by IPCC emission accounting protocols, we systematically evaluate spatiotemporal distribution patterns of agricultural CH₄ and N₂O emissions across 18 county-level units from 2006 to 2020. The investigation advances through two sequential phases: Mechanistic drivers analysis: employing the STIRPAT model, we quantify bidirectional effects (positive/negative) of critical determinants—including agricultural mechanization intensity and grain productivity—on CH₄/N₂O emission fluxes. Pathway scenario prediction: We construct three developmental scenarios (low-carbon transition, business-as-usual, and high-resource dependency) integrated with regional planning parameters. This framework enables the identification of optimal peaking chronologies for each county and proposes gradient peaking strategies through spatial zoning, thereby resolving fragmented carbon governance in agrarian counties. Methodologically, we establish a multi-scenario simulation architecture incorporating socioeconomic growth thresholds and agroecological constraints. The derived decision-support system provides empirically grounded solutions for aligning subnational climate actions with global mitigation targets.