Qinghai Song

Fog is an important meteorological feature of montane cloud forests and strongly influences ecosystem carbon and water exchanges, yet its seasonal ecological effects remain poorly understood in subtropical forests. Here, we combined three years (2021–2023) of eddy covariance, visibility, and micrometeorological observations to quantify seasonal fog dynamics and evaluate ecosystem functioning in the Ailao Mountain subtropical evergreen broad-leaved forest, southwestern China. Approximately 60% of fog events occurred during the rainy season, whereas their ecological effects differed markedly between seasons. Fog significantly reduced air temperature (Tair), photosynthetically active radiation (PAR), and vapor pressure deficit (VPD), while only slightly increasing soil volumetric water content (VWC), indicating that it primarily modified the canopy atmospheric environment. During the dry season, fog reduced evapotranspiration (ET) by 28.26% (p < 0.01) without significantly affecting gross primary productivity (GPP), resulting in a 57.52% increase in ecosystem water-use efficiency (WUE). In contrast, during the rainy season, fog suppressed GPP by 34.89% (p < 0.01), whereas ET decreased by only 6.20%, producing no significant change in WUE. Despite a 60–65% reduction in PAR, light-use efficiency (LUE) increased significantly under foggy conditions, with a greater enhancement during the dry season (86.34%) than during the rainy season (56.34%). Under non-foggy conditions, carbon and water fluxes were primarily regulated by radiation- and temperature-related variables, whereas under foggy conditions soil water availability dominated ET and LUE. These findings demonstrate that fog influences ecosystem functioning through both immediate microclimatic modification and changes in the environmental regulation of carbon–water coupling.

Introduction: The deployment of digital cameras on flux towers offers a practical approach to exploring how seasonal canopy photosynthesis is related to canopy color indices (CCIs). However, the pronounced spatial and temporal heterogeneity of savanna ecosystems remains a major challenge. Methods: We compared EC-derived gross primary production (GPP) with six LUE model variants, including two direct-FPAR models, three CCI-proxy models, and one MODIS NDVI-proxy model. This framework allowed us to distinguish direct FPAR inputs from vegetation-index proxies for the FPAR term in LUE-based GPP estimation. Results: During the extreme high-temperature drought of 2019, the peak values of S _green , Hue, and GEI decreased by approximately 50%. Annual GPP declined from 850 to 567 g C m ⁻² , leaf area index (LAI) decreased from 0.989 to 0.670 m ² m ⁻² , and LUE decreased from 0.40 to 0.25 g C MJ ⁻¹ . The seasonal pattern of GPP remained broadly consistent, although the length of the growing season (LOS) varied substantially, ranging from 213 to 111 d in 2016 and 2019, respectively. During the growing season (GS), the CCIs were closely synchronized with daily GPP and LAI; during the non-growing season (NGS), a lag of approximately 20 days was observed. Among the six LUE variants, the MODIS NDVI-proxy model performed best ( R ² = 0.77, slope = 1.19), whereas the MODIS FPAR model showed moderate performance and underestimated GPP ( R ² = 0.61, slope = 0.66). The tower-derived FPAR model overestimated GPP, and the CCI-proxy models performed moderately, especially with lower accuracy during the non-growing season. Discussion: Overall, digital camera-derived CCIs can be used as empirical FPAR-like proxies for estimating seasonal GPP dynamics in savanna ecosystems, but they should not be interpreted as physical FPAR measurements. The comparison with tower-derived FPAR and MODIS FPAR highlights the importance of explicitly distinguishing direct FPAR inputs from vegetation-index proxies in LUE-based GPP modeling.

Tropical forests play a pivotal role in the global carbon cycle, but the lack of long-term in-situ datasets renders our understanding of the specific carbon dynamics in tropical forests uncertain. In this study, we analyzed two decades (2003–2022) of eddy-covariance measurements from a primary tropical seasonal rainforest reserve in Xishuangbanna, southwest China, to characterize long-term trends in gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem productivity (NEP). The protected rainforest functioned as a modest but steadily strengthening carbon sink (annual mean NEP = 157.9 ± 56.7 g C m⁻² year⁻¹, growth rate = 3.4% year⁻¹), consistent with an observed increase in carbon use efficiency (CUE) (annual mean CUE = 5.9% ± 1.8%, growth rate = 2.4% year-1), which reflects increasingly efficient carbon utilization and aligns with rising aboveground biomass. The enhancement of the interannual carbon sink was mainly driven by increasing GPP (mean = 2658.1 ± 254.5 g C m⁻² year⁻¹, growth rate = 1.0% year⁻¹). With the same 6-month duration, the tropical seasonal rainforest exhibited a stronger carbon sink during the dry season (148.3 g C m-1 season-1) than during the rainy season, with the dry season accounting for 93.9% of the annual carbon sink. The enhanced dry season radiation and precipitation throughout the two decades positively affected the upward trend of the carbon sink. Notably, the annual carbon sink showed a temporary decline approximately two years after droughts, suggesting a lagged ecosystem response to climatic disturbances. Overall, these findings underscore the long-term carbon sequestration potential of well-preserved tropical rainforests and provide critical empirical evidence for improving carbon budget assessments in tropical regions under ongoing climate change.

Global climate change is intensively altering precipitation regimes, with profound consequences for the structure and function of various terrestrial ecosystems. Soil microbes are a key driver of organic matter decomposition and nutrient cycling; however, their response mechanisms to precipitation variations in fragile ecosystems remain poorly understood. We conducted an in situ precipitation manipulation experiment in a savanna ecosystem within the Yuanjiang dry-hot valley of southwest China since January 2014. We established three treatments: a control plot with natural precipitation (NP), precipitation exclusion by 50% (PE50), and precipitation addition by 50% (PA50). Soil samples were collected in mid-April and mid-August 2025. Using high-throughput sequencing technology, we systematically examined how precipitation variations affected soil microbial community structure and the underlying environmental drivers. The study results showed that both PA50 and PE50 treatments significantly altered the α- and β-diversity of bacterial and fungal communities (PERMANOVA, p < 0.05), marking a clear separation in overall soil microbial community structure among treatments. The bacterial community response was more pronounced to precipitation variations than the fungal community, and exhibited a non-linear response pattern. Both PE50 and PA50 treatments increased bacterial richness. In contrast, shifts in fungal diversity were season-dependent. The analysis results of Linear discriminant analysis Effect Size (LEfSe) revealed that the PE50 treatment enriched drought-tolerant taxa, such as Actinobacteria and Ascomycota. Conversely, the PA50 treatment favored moisture-preferring taxa, including Acidobacteria and Basidiomycota. Redundancy analysis (RDA) identified soil moisture (SM), dissolved organic nitrogen (DON), and soil organic carbon (SOC) as the key factors driving these community shifts. The relative importance of these drivers varied seasonally: SM was dominant in the dry season, while SOC and nutrient-related factors prevailed during the rainy season. This study elucidates the non-linear and seasonally contingent response mechanisms of soil microbial communities to precipitation variations in a fragile savanna ecosystem. Our findings provide a critical theoretical framework for predicting how the structure and function of vulnerable ecosystems may evolve under future climate change.

Increasing drought frequency and intensity affect biophysical functions of natural ecosystems. In tropical semi‐arid savannas, while immediate drought effects are well‐studied, the drought legacy effects on vegetation composition and associated ecosystem functions remain unclear. We used data of vegetation composition, net ecosystem CO ₂ exchange, surface albedo and evapotranspiration (ET) in 2017–2022 from a savanna ecosystem, Southwest China, to investigate the legacy effect of an extreme drought event that occurred in 2019. Vegetation declined continuously for 3 post‐drought years. While tree numbers declined by 12%, shrub numbers dropped by 50% compared with pre‐drought levels, shifting vegetation dominance toward trees. This structural change caused sustained reductions in albedo and ET, which remained below pre‐drought levels, despite gross primary production recovering in the years immediately post‐drought. Vegetation shifts disproportionately impact ecosystem functions, with energy and water fluxes exhibiting greater vulnerability and potentially enhancing regional warming as droughts increase in Asian savannas.

Nocturnal water consumption (NWC), known as including stem refilling (SR) and nocturnal transpiration (NT), has been documented in many plant species, but we do not yet have a clear understanding of species differences and the biotic and abiotic regulation of this phenomenon, especially for subtropical and tropical plantations. In this study, we examine the magnitude, seasonality, and biotic and abiotic regulation of NWC, SR, and NT in three widely planted subtropical and tropical species, Eucalyptus spp., Hevea brasiliensis, and Castanopsis hystrix, through the measured sap and meteorological variables. Stand-level NWC and SR differ significantly among the three plantations, where the mean daily NWC and SR of Eucalyptus spp. (2022–2023), Hevea brasiliensis (2014), and Castanopsis hystrix (2022–2023) are 0.08 mm and 0.05 mm, 0.36 mm and 0.28 mm, and 0.14 mm and 0.12 mm, respectively. Their stand-level NT values are 0.03 mm, 0.08 mm, and 0.02 mm, respectively. Additionally, distinct differences in the seasonality of NWC, SR, and NT are observed among the three plantations, with higher values during spring and autumn and lower values in summer and winter. SR is identified as the predominant role in NWC for all the plantations. VPD is identified as the primary meteorological factor driving NWC, SR, and NR in Eucalyptus spp. and Hevea brasiliensis plantations, while no prominent abiotic variables show the main driver in Castanopsis hystrix. Our findings reveal important implications for the NWC of tropical plantations related to soil–plant–atmosphere equilibrium and hydrology modeling.

To explore the response of soil greenhouse gas emissions(GHGs) from tropical forest to landuse change in Yunnan, Southwest China, we have conducted a series of studies based on the GHGs monitoring platform established since 2003 in tropical rainforest (TRF) and rubber plantation(RP). The research results indicate that 1) TRF transplanted to RP did not change the annual soil CO2 emissions (TRF, 359 ±91 and RP 352 ±41 mg CO2 m-2 h -1) but decreased soil CH4 uptake significantly (TRF, -0.11 ± -0.18 mg CH4 m -2 h -1; RP, -0.020 ± -0.087 mg CH4 m-2 h-1). (2) The most important influence on soil CO2 and CH4 emissions in the RP was the leaf area index and soil water content, respectively, whereas the soil water content, soil temperature, and dead fine roots were the most important factors in the TRF. Variations in the soil CO2 and CH4 caused by landuse transition were individually explained by soil temperature and fine root growth and decomposition, respectively. (3) The N2O emissions from the fertilized and unfertilized plots in RP were 4.0 and 2.5 kg N ha−1 yr−1, respectively; Annual N2O emissions from the control and no litter input treatments were 0.48 and 0.32 kg N2O–N ha-1 year in TRF, respectively.(4) When entire land area in Xishuangbanna is considered, N2O emissions from fertilized rubber plantations offset 17.1% of the tropical rainforest’s carbon sink. The results show that if tropical rainforests are converted to fertilized rubber plantations, regional N2O emissions may enhance local climate warming. (5) And further, land use change alter the structure and sources of soil organic matter, which in turn feedback to the microbial processes involved in soil greenhouse gas production and alter the mechanisms of soil greenhouse gas emissions.(6) The 15N isotope tracing experiment used isotope tracing technology to distinguish the microbial process of N2O production in tropical rainforest soil, proving that the microbial process of N2O production in tropical rainforest soil during the dry season is a nitrification process; In the future, we will use 13C,14C and 15N isotope and qPCR to study the microbiological mechanisms of land use change on soil greenhouse gas production in the context of climate change, providing scientific basis for quantifying the underground processes of soil greenhouse gas production; Provide mechanism support for accurately estimating soil greenhouse gas emissions to achieve the dual carbon goals in the context of climate change.

Latent heat flux (LE) is a measure of the water exchange between Earth's surface and atmosphere, also known as evapotranspiration. It is a fundamental component in the Earth's energy budget and hydrological cycle and plays an important role in regulating the weather and climate. Moderate Resolution Imaging Spectroradiometer (MODIS) offers a gap‐filled biophysical product for LE at 8‐day temporal and 500‐meter spatial resolutions. Nonetheless, validation against the in situ eddy covariance measurement reveals significant errors in MODIS LE estimation. Our study integrates ground‐measured, reanalysis and satellite data to predict LE by leveraging the advantage of the data‐driven method. The study draws upon flux data derived from the AsiaFlux database, alongside reanalysis datasets from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and the European Centre for Medium‐Range Weather Forecasts (ERA5) products, as well as biophysical measurements from the MODIS satellite. An analysis of the annual water budget, based on ERA5 precipitation data, highlights net positive water balances across the study sites. By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. This study highlights the robustness of support vector regression and accentuates the impact of climatic and environmental conditions on model performance, ultimately contributing to more precise predictions of latent heat flux.

为了探究物候指数与涡度相关技术获取的通量数据之间的关系，本研究比较了中国西南部热带雨林（BNS）和泰国干性龙脑香林（DPT）通量塔获取的两年的监测数据。研究周期从2020年1月到2022年4月。基于红、绿、蓝数字值（RGBDN）提取的冠层颜色指数分为RGB色坐标（RGBCC；RCC、GCC、BCC）、绿色绝对指数（GEI）、植被对比指数（VCI）的RGBratio、绿红比（GRr）和红蓝比（RBr）。结果显示，植被指数呈现明显的季节变化，表征了热带森林的物候特征。在BNS站点，植被指数与总初级生产力（GPP）呈显著正相关关系（GRr: R2=0.91, GEI: R2=0.90, GCC:R2=0.88, VCI: R2=0.87; P<0.05），而DPT站点显示出比BNS更弱的关系（VCI: R2=0.77, GCC: R2=0.77, GEI: R2=0.71, GRr: R2=0.45）。BNS（热带雨林）对物候变化的敏感性高于DPT（干性龙脑香林），同时热带雨林的GPP (5.92~7.79 gC/(m2·day)) 高于干性龙脑香林的 (4.64~5.63 gC/(m2·day)) 。土壤温度可能是驱动BNS物候变化的重要因素，而相对湿度可能对DPT森林的物候具有更重要的影响。 长期数字相机观测为GPP评估和理解树冠物候对气候变化响应提供了宝贵数据，未来研究应加强对热带森林生态系统冠层物候变化和生态系统碳交换量的定量关系的理解。

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPP_HLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPP_MODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPP_EC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.