Exploring complex water stress-gross primary production relationships: Impact of climatic drivers, main effects, and interactive effects

Elevation dependence of vegetation growth stages and carbon sequestration dynamics in high mountain ecosystems

Mountain vegetation exhibits unique growth strategies along elevation gradients to adapt to climatic constraints. However, the mechanisms by which temporal allocation of photosynthetic phases and climate change drive elevational differentiation in carbon sequestration dynamics remain poorly quantified. This study adopted a phenology-based method to divide the photosynthetic growing season into distinct stages, revealing vegetation carbon allocation and its elevation-dependent patterns at a finer temporal resolution. In the Qilian Mountains (QLMs), although the maturity period was only 1.2 times longer than the greening period, it contributed fivefold greater gross primary productivity (GPP) during growing season, highlighting its pivotal role in GPP dynamics. Notably, GPP during both the photosynthetic growing season and maturity period exhibited greater relative rates of change at high elevations (>3500 m) than at lower elevations (2500-3500 m). Concurrently, vegetation at higher elevations displayed greater temperature sensitivity. For every 1000 m increase in elevation, the maturity period lengthened by 3.4%, while the greening and senescence periods shortened, maximizing carbon sequestration under colder conditions. Analysis through boosted regression trees and partial least squares regression revealed a dual-control mechanism governing GPP through hydrothermal conditions and growth-stage duration. Temperature dominated GPP during growing season and maturity period, whereas growth-stage duration exerted predominant influence on greening and senescence periods. The observed trend of vegetation homogenization along the elevation gradient in the QLMs could reduce ecosystem resilience and carbon sequestration capacity. Continued monitoring and research are crucial for understanding these impacts and guiding ecosystem management in high-altitude regions.

Nonlinear dynamics of ecosystem productivity and its driving mechanisms in arid regions: A case study of Ebinur Lake Basin

Arid zone ecosystems exhibit significant sensitivity to climate change and human activities, often demonstrating pronounced nonlinear characteristics in their response processes. This study focuses on the typical inland basin of the Ebinur Lake Basin in the arid region of northwest China. Utilizing long-term remote sensing and meteorological data from 1982 to 2018, combined with an improved BFAST algorithm, the XGBoost-SHAP analytical framework, and Partial Least Squares Structural Equation Modeling (PLS-SEM), we systematically investigated the nonlinear variation characteristics of Gross Primary Productivity (GPP) and its underlying driving mechanisms. The results reveal that tipping points in GPP changes were detected in 99.78 % of the Ebinur Lake Basin, with the "Negative Reversal" type (initially increasing and then decreasing) being the most prevalent, accounting for 35.91 % of the cases. The majority of GPP tipping points occurred between 1998 and 2002, with the highest frequency observed in 1998 (10.54 %). Vapor pressure deficit (VPD) was identified as the primary factor controlling GPP changes in the Ebinur Lake Basin. However, in areas where the ecosystem exhibited a recovery trend, temperature surpassed VPD as the dominant driving factor, indicating that improved temperature conditions are critical for productivity restoration. These findings enhance our understanding of the nonlinear characteristics and underlying mechanisms of arid zone ecosystems, providing a scientific basis for adaptive ecosystem management in the region.

Plant traits shape global spatiotemporal variations in photosynthetic efficiency

Photosynthetic efficiency (PE) quantifies the fraction of absorbed light used in photochemistry to produce chemical energy during photosynthesis and is essential for understanding ecosystem productivity and the global carbon cycle, particularly under conditions of vegetation stress. However, nearly 60% of the global spatiotemporal variance in terrestrial PE remains unexplained. Here we integrate remote sensing and eco-evolutionary optimality theory to derive key plant traits, alongside explainable machine learning and global eddy covariance observations, to uncover the drivers of daily PE variations. Incorporating plant traits into our model increases the explained daily PE variance from 36% to 80% for C3 vegetation and from 54% to 84% for C4 vegetation compared with using climate data alone. Key plant traits-including chlorophyll content, leaf longevity and leaf mass per area-consistently emerge as important factors across global biomes and temporal scales. Water availability and light conditions are also critical in regulating PE, underscoring the need for an integrative approach that combines plant traits with climatic factors. Overall, our findings demonstrate the potential of remote sensing and eco-evolutionary optimality theory to capture principal PE drivers, offering valuable tools for more accurately predicting ecosystem productivity and improving Earth system models under climate change.

Dual controls of vapour pressure deficit and soil moisture on photosynthesis in a restored temperate bog

Despite only covering ~3 % of the land mass, peatlands store more carbon (C) per unit area than any other ecosystem. This is due to the discrepancy between C fixed by the plants (Gross primary productivity (GPP)) and decomposition. However, this C is vulnerable to frequent, severe droughts and changes in the peatland microclimate. Plants play a vital role in ecosystem C dynamics under drought by mediating water loss to the atmosphere (surface water vapour conductance) and GPP by the presence/absence of stomatal regulation. This is dependent on soil moisture, air temperature, and vapour pressure deficit (VPD). Although there is ample evidence of the role of VPD on stomatal regulation and GPP, the impact of soil moisture is still debated. We addressed this knowledge gap by investigating the role of bulk surface conductance of water vapour in shifts between climatic (Air temperature (Tair), incoming shortwave radiation (SWR) and VPD) and water limitation of GPP in a peat bog in Canada. A causal analysis process was used to investigate how environmental factors influenced GPP. The results suggested that stomatal regulation in response to increased VPD caused the reduction in GPP in 2016 (~2.5 gC m-2 day-1 as opposed to ~3 gC m-2 day-1 in 2018). In contrast, GPP was limited again in 2019 due to the dry surface. This was driven by the relaxed stomatal regulation adopted by the ecosystem following the initial drought to maximise C assimilation. We found the threshold at which surface water decline limited GPP was at about -8 cm water table depth (82.5 % soil moisture). The causal inference corroborated our findings. The temporal variations of water and energy limitation seen in this study could increasingly restrict GPP due to the projected climate warming.

A greater negative impact of future climate change on vegetation in Central Asia: Evidence from trajectory/pattern analysis

In the context of global warming, vegetation changes exhibit various patterns, yet previous studies have focused primarily on monotonic changes, often overlooking the complexity and diversity of multiple change processes. Therefore, it is crucial to further explore vegetation dynamics and diverse change trajectories in this region under future climate scenarios to obtain a more comprehensive understanding of local ecosystem evolution. In this study, we established an integrated machine learning prediction framework and a vegetation change trajectory recognition framework to predict the dynamics of vegetation in Central Asia under future climate change scenarios and identify its change trajectories, thus revealing the potential impacts of future climate change on vegetation in the region. The findings suggest that various future climate scenarios will negatively affect most vegetation in Central Asia, with vegetation change intensity increasing with increasing emission trajectories. Analyses of different time scales and trend variations consistently revealed more pronounced downward trends. Vegetation change trajectory analysis revealed that most vegetation has undergone nonlinear and dramatic changes, with negative changes outnumbering positive changes and curve changes outnumbering abrupt changes. Under the highest emission scenario (SSP5-8.5), the abrupt vegetation changes and curve changes are 1.7 times and 1.3 times greater, respectively, than those under the SSP1-2.6 scenario. When transitioning from lower emission pathways (SSP1-2.6, SSP2-4.5) to higher emission pathways (SSP3-7.0, SSP5-8.5), the vegetation change trajectories shift from neutral and negative curve changes to abrupt negative changes. Across climate scenarios, the key climate factors influencing vegetation changes are mostly evapotranspiration and soil moisture, with temperature and relative humidity exerting relatively minor effects. Our study reveals the negative response of vegetation in Central Asia to climate change from the perspective of vegetation dynamics and change trajectories, providing a scientific basis for the development of effective ecological protection and climate adaptation strategies.

Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China

Reservoir nearshore areas are influenced by both terrestrial and aquatic ecosystems, making them sensitive regions to water quality changes. The analysis of basin landscape hydrological features provides limited insight into the spatial heterogeneity of eutrophication in these areas. The complex characteristics of shoreline morphology and their impact on eutrophication are often overlooked. To comprehensively analyze the complex relationships between shoreline morphology and landscape hydrological features, with eutrophication, this study uses Danjiangkou Reservoir as a case study. Utilizing Landsat 8 OLI remote sensing data from 2013 to 2022, combined with a semi-analytical approach, the spatial distribution of the Trophic State Index (TSI) during flood discharge periods (FDPs) and water storage periods (WSPs) was obtained. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), explained the relationships between landscape composition, landscape configuration, hydrological topography, shoreline morphology, and TSI, identified key factors at different spatial scales and validated their reliability. The results showed that: (1) There is significant spatial heterogeneity in the TSI distribution of Danjiangkou Reservoir. The eutrophication levels are significant in the shoreline and bay areas, with a tendency to extend inward only during the WSPs. (2) The importance of landscape composition, landscape configuration, hydrological topography, and shoreline morphology to TSI variations during the FDPs are 25.12 %, 29.6 %, 23.09 %, and 22.19 % respectively. Besides shoreline distance, the Landscape Shape Index (LSI) and Hypsometric Integral (HI) are the two most significant environmental variables overall during the FDPs. Forest and grassland areas become the most influential factors during the WSPs. The influence of landscape patterns and hydrological topography on TSI varies at different spatial scales. At the 200 m riparian buffer zone, the increase in cropland and impervious areas significantly elevates eutrophication levels. (3) Morphology complexity, shows a noticeable threshold effect on TSI, with complex shoreline morphology increasing the risk of eutrophication.

Resistance of grassland productivity to drought and heatwave over a temperate semi-arid climate zone

Drought and heatwave are the primary climate extremes for vegetation productivity loss in the global temperate semi-arid grassland, challenging the ecosystem productivity stability in these areas. Previous studies have indicated a significant decline in the resistance of global grassland productivity to drought, but we still lack a systematic understanding of the mechanisms determining the spatiotemporal variations in grassland resistance to drought and heatwave. In this study, we focused on temperate semi-arid grasslands of China (TSGC) to assess the spatiotemporal variations of grassland productivity resistance to different climate extremes: compound dry-hot events, individual drought events, and individual heatwave events that occurred during 2000-2019. Based on the explainable machine learning model, we explored the resistance to the interaction of drought and heatwave and identify the dominant factors determining the spatiotemporal variations in resistance. The results revealed that grassland resistance to climate extremes had decreased in Xilingol Grassland and Mu Us Sandy Land, and had a not significant increase in Otindag Desert during 2000-2019. Human activities and the increase in CO2 concentration causes a decline in resistance in Mu Us Sandy Land, and the increase of VPD and shift of vegetation loss event timing caused a decline in resistance in Xilingol Grassland, while the weakening of climate extremes, especially the shortening of drought duration, increase the resistance in Otindag Desert. Mean annual temperature dominates the spatial differences in resistance among different grasslands. When drought and heatwave occur simultaneously, there is an additive effect on resistance and causes lower resistance to compound dry-hot events compared to individual drought and heatwave events. Our analysis provides crucial insights into understanding the impact of climate extremes on the temperate semi-arid grasslands of China.

Tropospheric ozone pollution increases the sensitivity of plant production to vapor pressure deficit across diverse ecosystems in the Northern Hemisphere

Tropospheric ozone (O3) pollution often accompanies droughts and heatwaves, which could collectively reduce plant productivity. Previous research suggested that O3 pollution can alter plant responses to drought by interfering with stomatal closure while drought can reduce stomatal conductance and provide protection against O3 stress. However, the interactions between O3 pollution and drought stress remain poorly understood at ecosystem scales with diverse plant functional types. To address this research gap, we used 10-year (2012-2021) satellite near-infrared reflectance of vegetation (NIRv) observations, reanalysis data of vapor pressure deficit (VPD), soil moisture (SM), and air temperature (Ta), along with O3 measurements and reanalysis data across the Northern Hemisphere to statistically disentangle the interconnections between NIRv, VPD, SM, and Ta under varying O3 levels. We found that high O3 concentrations significantly exacerbate the sensitivity of NIRv to VPD while have no notable impacts on the sensitivity of NIRv to Ta or SM for all plant functional types, indicating an enhanced combined impact of VPD and O3 on plants. Specifically, the sensitivity of NIRv to VPD increased by >75 % when O3 anomalies increased from the lowest 10 to the highest 10 percentiles across diverse plant functional types. This is likely because long-term exposure to high O3 concentrations can inhibit stomatal closure and photosynthetic enzyme activities, resulting in reduced water use efficiency and photosynthetic efficiency. This study highlights the need to consider O3 in understanding plant responses to climate factors and that O3 can alter plant responses to VPD independently of Ta and SM.

Evidence for the acclimation of ecosystem photosynthesis to soil moisture

Ecosystem gross primary productivity (GPP) is the largest carbon flux between the atmosphere and biosphere and is strongly influenced by soil moisture. However, the response and acclimation of GPP to soil moisture remain poorly understood, leading to large uncertainties in characterizing the impact of soil moisture on GPP in Earth system models. Here we analyze the GPP-soil moisture response curves at 143 sites from the global FLUXNET. We find that GPP at 108 sites exhibits hump-shaped response curves with increasing soil moisture, and an apparent optimum soil moisture (SM opt GPP , at which GPP reaches the maximum) exists widely with large variability among sites and biomes around the globe. Variation inSM opt GPP is mostly explained by local water availability, with drier ecosystems having lowerSM opt GPP than wetter ecosystems, reflecting the water acclimation ofSM opt GPP . This acclimation is further supported by a field experiment that only manipulates water and keeps other factors constant, which shows a downward shift inSM opt GPP after long-term water deficit, and thus a lower soil water requirement to maximize GPP. These results provide compelling evidence for the widespreadSM opt GPP and its acclimation, shedding new light on understanding and predicting carbon-climate feedbacks.

Fertilization regulates global thresholds in soil bacteria

Global patterns in soil microbiomes are driven by non-linear environmental thresholds. Fertilization is known to shape the soil microbiome of terrestrial ecosystems worldwide. Yet, whether fertilization influences global thresholds in soil microbiomes remains virtually unknown. Here, utilizing optimized machine learning models with Shapley additive explanations on a dataset of 10,907 soil samples from 24 countries, we discovered that the microbial community response to fertilization is highly dependent on environmental contexts. Furthermore, the interactions among nitrogen (N) addition, pH, and mean annual temperature contribute to non-linear patterns in soil bacterial diversity. Specifically, we observed positive responses within a soil pH range of 5.2-6.6, with the influence of higher temperature (>15°C) on bacterial diversity being positive within this pH range but reversed in more acidic or alkaline soils. Additionally, we revealed the threshold effect of soil organic carbon and total nitrogen, demonstrating how temperature and N addition amount interacted with microbial communities within specific edaphic concentration ranges. Our findings underscore how complex environmental interactions control soil bacterial diversity under fertilization.

Elevated aerosol enhances plant water-use efficiency by increasing carbon uptake while reducing water loss

Aerosols could significantly influence ecosystem carbon and water fluxes, potentially altering their interconnected dynamics, typically characterized by water-use efficiency (WUE). However, our understanding of the underlying ecophysiological mechanisms remains limited due to insufficient field observations. We conducted 4-yr measurements of leaf photosynthesis and transpiration, as well as 3-yr measurements of stem growth (SG) and sap flow of poplar trees exposed to natural aerosol fluctuation, to elucidate aerosol's impact on plant WUE. We found that aerosol improved sun leaf WUE mainly because a sharp decline in photosynthetically active radiation (PAR) inhibited its transpiration, while photosynthesis was less affected, as the negative effect induced by declined PAR was offset by the positive effect induced by low leaf vapor pressure deficit (VPDleaf). Conversely, diffuse radiation fertilization (DRF) effect stimulated shade leaf photosynthesis with minimal impact on transpiration, leading to an improved WUE. The responses were further verified by a strong DRF on SG and a decrease in sap flow due to the suppresses in total radiation and VPD. Our field observations indicate that, contrary to the commonly assumed coupling response, carbon uptake and water use exhibited dissimilar reactions to aerosol pollution, ultimately enhancing WUE at the leaf and canopy level.

Understanding the effects of flash drought on vegetation photosynthesis and potential drivers over China

Flash droughts characterized by rapid onset and intensification are expected to be a new normal under climate change and potentially affect vegetation photosynthesis and terrestrial carbon sink. However, the effects of flash drought on vegetation photosynthesis and their potential dominant driving factors remain uncertain. Here, we quantify the susceptibility and response magnitude of vegetation photosynthesis to flash drought across different ecosystems (i.e., forest, shrubland, grassland, and cropland) in China based on reanalysis and satellite observations. By employing the extreme gradient boosting model, we also identify the dominant factors that influence these flash drought-photosynthesis relationships. We show that over 51.46 % of ecosystems across China are susceptible to flash drought, and grasslands are substantially suppressed, as reflected in both sensitivity and response magnitude (with median gross primary productivity anomalies of -0.13). We further demonstrate that background climate differences (e.g., mean annual temperature and aridity) predominantly regulate the response variation in forest and shrubland, with hotter/colder or drier ecosystems being more severely suppressed by flash drought. However, in grasslands and croplands, the differential vegetation responses are attributed to the intensity of abnormal hydro-meteorological conditions during flash drought (e.g., vapor pressure deficit (VPD) and temperature anomalies). The effects of flash droughts intensify with increasing VPD and nonmonotonically relate to temperature, with colder or hotter temperatures leading to more severe vegetation loss. Our results identify the vulnerable ecological regions under flash drought and enable a better understanding of vegetation photosynthesis response to climate extremes, which may be useful for developing effective management strategies.

Controlling factors of heavy metal(loid) accumulation in rice: Main and interactive effects

Identifying the key determinants of heavy metal(loid) accumulation in rice and quantifying their contributions are critical for precise prediction of heavy metal(loid) concentrations in rice and the formulation of effective pollution control strategies. The accumulation of heavy metal(loid)s in rice can be influenced by both natural and anthropogenic factors, which may interact with each other. However, distinguishing the independent roles (main effects) from interactive effects and quantifying their impacts separately pose challenges. To address this knowledge gap, we employed TreeExplainer-based SHAP and random forest algorithms in this study to quantitatively estimate the primary influencing factors and their main and interactive effects on heavy metal(loid)s in rice. Our findings reveal that soil cadmium (SCd) and rice cultivation time (C_TIME) were the primary contributors to rice cadmium (RCd) and rice arsenic (RAs), respectively. Soil lead (SPb) and sampling distances from roads significantly contributed to rice lead (RPb). Additionally, we identified significant interactive effects of SCd and C_TIME, C_TIME and RCd, and RCd and rice variety on RCd, RAs, and RPb, respectively, emphasizing their significance. These insights are pivotal in improving the accuracy of heavy metal(loid) concentration predictions in rice and offering theoretical guidance for the formulation of pollution control measures.

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

Prioritizing ecological restoration in hydrologically sensitive areas to improve groundwater quality

Greening is the optimal way to mitigate climate change and water quality degradation caused by agricultural expansion and rapid urbanization. However, the ideal sites to plant trees or grass to achieve a win-win solution between the environment and the economy remain unknown. Here, we performed a nationwide survey on groundwater nutrients (nitrate nitrogen, ammonia nitrogen, dissolved reactive phosphorus) and heavy metals (vanadium, chromium, manganese, iron, cobalt, nickel, copper, arsenic, strontium, molybdenum, cadmium, and lead) in China, and combined it with the global/national soil property database and machine learning (random forest) methods to explore the linkages between land use within hydrologically sensitive areas (HSAs) and groundwater quality from the perspective of hydrological connectivity. We found that HSAs occupy approximately 20 % of the total land area and are hotspots for transferring nutrients and heavy metals from the land surface to the saturated zone. In particular, the proportion of natural lands within HSAs significantly contributes 8.0 % of the variability in groundwater nutrients and heavy metals in China (p < 0.01), which is equivalent to their contribution (8.8 %) at the regional scale (radius = 4 km, area = 50 km2). Increasing the proportion of natural lands within HSAs improves groundwater quality, as indicated by the significant reduction in the concentrations of nitrate nitrogen, manganese, arsenic, strontium, and molybdenum (p < 0.05). These new findings suggest that prioritizing ecological restoration in HSAs is conducive to achieving the harmony between the environment (improving groundwater quality) and economy (reducing investment in area management).

Global change progressively increases foliar nitrogen-phosphorus ratios in China's subtropical forests

Globally increased nitrogen (N) to phosphorus (P) ratios (N/P) affect the structure and functioning of terrestrial ecosystems, but few studies have addressed the variation of foliar N/P over time in subtropical forests. Foliar N/P indicates N versus P limitation in terrestrial ecosystems. Quantifying long-term dynamics of foliar N/P and their potential drivers is crucial for predicting nutrient status and functioning in forest ecosystems under global change. We detected temporal trends of foliar N/P, quantitatively estimated their potential drivers and their interaction between plant types (evergreen vs. deciduous and trees vs. shrubs), using 1811 herbarium specimens of 12 widely distributed species collected during 1920-2010 across China's subtropical forests. We found significant decreases in foliar P concentrations (23.1%) and increases in foliar N/P (21.2%). Foliar N/P increased more in evergreen species (22.9%) than in deciduous species (16.9%). Changes in atmospheric CO2 concentrations (P CO 2 $$ {\mathrm{P}}_{{\mathrm{CO}}_2} $$ ), atmospheric N deposition and mean annual temperature (MAT) dominantly contributed to the increased foliar N/P of evergreen species, whileP CO 2 $$ {\mathrm{P}}_{{\mathrm{CO}}_2} $$ , MAT, and vapor pressure deficit, to that of deciduous species. Under future Shared Socioeconomic Pathway (SSP) scenarios, increasing MAT andP CO 2 $$ {\mathrm{P}}_{{\mathrm{CO}}_2} $$ would continuously increase more foliar N/P in deciduous species than in evergreen species, with more 12.9%, 17.7%, and 19.4% versus 6.1%, 7.9%, and 8.9% of magnitudes under the scenarios of SSP1-2.6, SSP3-7.0, and SSP5-8.5, respectively. The results suggest that global change has intensified and will progressively aggravate N-P imbalance, further altering community composition and ecosystem functioning of subtropical forests.

Climatic limitations on grassland photosynthesis over the Tibetan Plateau shifted from temperature to water

Plant photosynthesis plays an essential role in regulating the global carbon cycle. Therefore, it is essential to understand the limitations imposed by climate on plant photosynthesis to comprehend the impacts of climate change on land carbon dynamics. In this study, taking gross primary productivity as a direct representation of photosynthesis, we employed a light use efficiency model (i.e., the revised EC-LUE) and factorial analysis method to quantify the spatiotemporal variation of temperature- and water-limitations on plant photosynthesis over the Tibetan Plateau (TP) grasslands during growing season (May to October) in 1983-2018. Results revealed a clear spatiotemporal pattern of the temperature- and water-limitations: temperature is the primary climatic limiting factor in the eastern TP, while water is the primary climatic limiting factor in the western TP; the water- and temperature-limitations prevail in summer and spring/autumn, respectively. The water- and temperature-limitations intensified and alleviated, respectively, during 1983 through 2018. There also was a widespread shift from temperature-limitation to water-limitation in the TP, particularly in midsummer (August). Our findings demonstrated the shifting relative importance of climatic limitations on plant photosynthesis under changing climate, which is crucial for predicting future terrestrial carbon cycle dynamics.

Anthropogenic disturbance exacerbates resilience loss in the Amazon rainforests

Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag-1 month temporal autocorrelation (TAC), to vegetation optical depth data in C-band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human-disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human-disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human-disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests.

Soil moisture dominates the variation of gross primary productivity during hot drought in drylands

The frequency and severity of hot drought will increase in the future due to impact of climate change and human activities, threatening the sustainability of terrestrial ecosystems and human societies. Hot drought is a typical type of drought event, high vapor pressure deficit (VPD) and low soil moisture (SM) are its main characteristics of hot drought, with increasing water stress on vegetation and exacerbating hydrological drought and ecosystem risks. However, our understanding of the effects of high VPD and low SM on vegetation productivity is limited, because these two variables are strongly coupled and influenced by other climatic drivers. The southwestern United States experienced one of the most severe hot drought events on record in 2020. In this study, we used SM and gross primary productivity (GPP) datasets from Soil Moisture Active and Passive (SMAP), as well as VPD and other meteorological datasets from gridMET. We decoupled the effects of different meteorological factors on GPP at monthly and daily scales using partial correlation analysis, partial least squares regression, and binning methods. We found that SM anomalies contribute more to GPP anomalies than VPD anomalies at monthly and daily scales. Especially at the daily scale, as the decoupled SM anomalies increased, the GPP anomalies increased. However, there is no significant change in GPP anomalies as VPD increases. For all the vegetation types and arid zones, SM dominated the variation in GPP, followed by VPD or maximum temperature. At the flux tower scale, decoupled soil water content (SWC) also dominated changes in GPP, compared to VPD. In the next century, hot drought will occur frequently in dryland regions, where GPP is one of the highest uncertainties in terrestrial ecosystems. Our study has important implications for identifying the strong coupling of meteorological factors and their impact on vegetation under climate change.

Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China

Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5-70, 2.15-35.02 and 1.76-31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural drought evolution and driving mechanism, which are beneficial for agricultural drought early warning and mitigation.

Enhanced dominance of soil moisture stress on vegetation growth in Eurasian drylands

Despite the mounting attention being paid to vegetation growth and their driving forces for water-limited ecosystems, the relative contributions of atmospheric and soil moisture dryness stress on vegetation growth are an ongoing debate. Here we comprehensively compare the impacts of high vapor pressure deficit (VPD) and low soil water content (SWC) on vegetation growth in Eurasian drylands during 1982-2014. The analysis indicates a gradual decoupling between atmospheric dryness and soil dryness over this period, as the former has expanded faster than the latter. Moreover, the VPD-SWC relation and VPD-greenness relation are both non-linear, while the SWC-greenness relation is near-linear. The loosened coupling between VPD and SWC, the non-linear correlations among VPD-SWC-greenness and the expanded area extent in which SWC acts as the dominant stress factor all provide compelling evidence that SWC is a more influential stressor than VPD on vegetation growth in Eurasian drylands. In addition, a set of 11 Earth system models projected a continuously growing constraint of SWC stress on vegetation growth towards 2100. Our results are vital to dryland ecosystems management and drought mitigation in Eurasia.

Interactive effects of UV radiation and water deficit on production characteristics in upland grassland and their estimation by proximity sensing

An increase in extreme weather and changes in other conditions associated with ongoing climate change are exposing ecosystems to a very wide range of environmental drivers that interact in ways which are not sufficiently understood. Such uncertainties in how ecosystems respond to multifactorial change make it difficult to predict the impacts of environmental change on ecosystems and their functions. Since water deficit (WD) and ultraviolet radiation (UV) trigger similar protective mechanisms in plants, we tested the hypothesis that UV modulates grassland acclimation to WD, mainly through changes in the root/shoot (R/S) ratio, and thus enhances the ability of grassland to acquire water from the soil and hence maintain its productivity. We also tested the potential of spectral reflectance and thermal imaging for monitoring the impacts of WD and UV on grassland production parameters. The experimental plots were manipulated by lamellar shelters allowing precipitation to pass through or to be excluded. The lamellas were either transmitting or blocking the UV. The results show that WD resulted in a significant decrease in aboveground biomass (AB). In contrast, belowground biomass (BB), R/S ratio, and total biomass (TB) increased significantly in response to WD, especially in UV exclusion treatment. UV exposure had a significant effect on AB and BB, but only in the last year of the experiment. The differences in the effect of WD between years show that the effect of precipitation removal is largely influenced by the potential evapotranspiration (PET) in a given year and hence mainly by air temperatures, while the resulting effect on production parameters is best correlated with the water balance given by the difference between precipitation and PET. Canopy temperature and selected spectral reflectance indices showed a significant response to WD and also significant relationships with morphological (AB, R/S) and biochemical (C/N ratio) parameters. In particular, the vegetation indices NDVI and RDVI provided the best correlations of biomass changes caused by WD and thus the highest potential to remotely sense drought effects on terrestrial vegetation.