Asymmetric responses of ecosystem productivity to rainfall anomalies vary inversely with mean annual rainfall over the conterminous United States

Nitrogen deposition differentially regulates the sensitivity of gross primary productivity to extreme drought versus wetness

Global hydroclimatic variability is increasing with more frequent extreme dry and wet years, severely destabilizing terrestrial ecosystem productivity. However, what regulates the consequence of precipitation extremes on productivity remains unclear. Based on a 9-year field manipulation experiment on the Qinghai-Tibetan Plateau, we found that the responses of gross primary productivity (GPP) to extreme drought and wetness were differentially regulated by nitrogen (N) deposition. Over increasing N deposition, extreme dry events reduced GPP more. Among the 12 biotic and abiotic factors examined, this was mostly explained by the increased plant canopy height and proportion of drought-sensitive species under N deposition, making photosynthesis more sensitive to hydraulic stress. While extreme wet events increased GPP, their effect did not shift over N deposition. These site observations were complemented by a global synthesis derived from the GOSIF GPP dataset, which showed that GPP sensitivity to extreme drought was larger in ecosystems with higher N deposition, but GPP sensitivity to extreme wetness did not change with N deposition. Our findings indicate that intensified hydroclimatic variability would lead to a greater loss of land carbon sinks in the context of increasing N deposition, due to that GPP losses during extreme dry years are more pronounced, yet without a synchronous increase in GPP gains during extreme wet years. The study implies that the conservation and management against climate extremes merit particular attention in ecosystems subject to N deposition.

Nitrogen addition does not alter symmetric responses of soil respiration to changing precipitation in a semi-arid grassland

Concurrent changing precipitation regimes and atmospheric nitrogen (N) deposition can have profound influences on soil carbon (C) cycling. However, how N enrichment regulates the responses of soil C fluxes to increasing variability of precipitation remains elusive. As part of a field precipitation gradient experiment with nine levels of precipitation amounts (-60 %, -45 %, -30 %, -15 %, ambient precipitation, +15 %, +30 %, +45 %, and +60 %) and two levels of N addition (0 and 10 g N m-2 yr-1) in a semi-arid temperate steppe on the Mongolian Plateau, this work was conducted to investigate the responses of soil respiration to decreased and increased precipitation (DP and IP), N addition, and their possible interactions. Averaged over the three years from 2019 to 2021, DP suppressed soil respiration by 16.1 %, whereas IP stimulated it by 27.4 %. Nitrogen addition decreased soil respiration by 7.1 % primarily via reducing microbial biomass C. Soil respiration showed symmetric responses to DP and IP within all the four precipitation variabilities (i.e., 15 %, 30 %, 45 %, and 60 %) under ambient N. Nevertheless, N addition did not alter the symmetric responses of soil respiration to changing precipitation due to the comparable sensitivities of microbial biomass and root growth to DP and IP under the N addition treatment. These findings indicate that intensified precipitation variability does not change but N addition could alleviate soil C releases. The unchanged symmetric responses of soil respiration to precipitation variability under N addition imply that N deposition may not change the response pattern of soil C releases to predicted increases in precipitation variability in grasslands, facilitating the robust projections of ecosystem C cycling under future global change scenarios.

Nitrogen rather than phosphorous addition alters the asymmetric responses of primary productivity to precipitation variability across a precipitation gradient on the northern Tibetan Plateau

Understanding the response of alpine grassland productivity to precipitation fluctuations is essential for assessing the future changes of ecosystem services. However, the underlying mechanism by which grassland productivity responds to wet and dry years after nitrogen (N) or/and phosphorus (P) nutrient addition remains unclear. In this study, we investigated the dynamics of plant communities based on eight-year N or/and P addition gradient experiments in four grassland types across a precipitation gradient on the north Tibetan Plateau. The asymmetry index (AI) was used to evaluate the responses of aboveground net primary productivity (ANPP) to precipitation fluctuations where AI > 0 indicates a greater increase of ANPP in wet years compared to the decline in dry years, and AI < 0 indicates a greater decline of ANPP in dry years compared to the increase in wet years. Our results showed that the AI values at community level in four natural grasslands were non-significant trend across the precipitation gradient, and showed slightly negative asymmetry, suggesting that the increase of ANPP in wet years was less than the decrease in dry years. N addition resulted in a significant decrease in community-level AI values with increasing mean annual precipitation (MAP), indicating that improved nutrient availability may favor the recovery of productivity in drier grasslands in wet years. At the functional group level, nutrient addition resulted in a significant decrease in the AI values of grasses and legumes and an increase in the AI values of forbs as MAP increased. Furthermore, the coupling of nutrients with precipitation can influence the productivity responses to precipitation changes by affecting soil nutrient availability and species richness. This research provides new insights into better predicting vegetation activity on N deposition rates and precipitation changes exacerbated in the context of climate change.

Aridity drives the variability of desert soil microbiomes across north-western China

Dryland covers >35 % of the terrestrial surface and the global extent of dryland increases due to the forecasted increase in aridity driven by climate change. Due to the climate change-driven aridity ecosystems, deserts provide one of the most hostile environments for microbial life and survival. Therefore, a detailed study was carried out to explore the deserts with different aridity levels (exposed to severe climate change) influence on microbial (bacteria, fungi, and protist) diversity patterns, assembly processes, and co-occurrence. The results revealed that the aridity (semi-arid, arid, and hyper-arid) patterns caused distinct changes in environmental heterogeneity in desert ecosystems. Similarly, microbial diversities were also reduced with increasing the aridity pattern, and it was found that environmental heterogeneity is highly involved in affecting microbial diversities under different ecological niches. Interestingly, it was found that certain microbes, including bacterial (Firmicutes), fungal (Sordariomycetes), and protistan (Ciliophora) abundance increased with increasing aridity levels, indicating that these microbes might possess the capability to tolerate the environmental stress conditions. Moreover, microbial community turnover analysis revealed that bacterial diversities followed homogenous selection, whereas fungi and protists were mostly driven by the dispersal limitation pattern. Co-occurrence network analysis showed that hyper-arid and arid conditions tightened the bacterial and fungal communities and had more positive associations compared to protistan. In conclusion, multiple lines of evidence were provided to shed light on the habitat specialization impact on microbial (bacteria, fungi, and protists) communities and composition under different desert ecosystems.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Water availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA-GPP ) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA-GPP relationship (RWA-GPP ) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near-infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance-light-use efficiency GPP. We found that the 3- and 7-year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17- and 40-year time scales (7.28% and 8.23%). The long-term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA-GPP and RWA-GPP with different source products: In high-latitude regions, RWA-GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid-low latitudes. Forests had weak RWA-GPP at all time scales, shrublands showed negative RWA-GPP at long time scales, and grassland (GL) showed a positive RWA-GPP at short time scales. Globally, the EWA-GPP , whether positive or negative, enhanced significantly at 3-, 7-, and 17-year time scales. For arid and humid zones, the semi-arid and sub-humid zones experienced a faster increase in the positive EWA-GPP , whereas the humid zones experienced a faster increase in the negative EWA-GPP . At the ecosystem types, the positive EWA-GPP at a 3-year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA-GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA-GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Ambient precipitation determines the sensitivity of soil respiration to precipitation treatments in a marsh

The effects in field manipulation experiments are strongly influenced by amplified interannual variation in ambient climate as the experimental duration increases. Soil respiration (SR), as an important part of the carbon cycle in terrestrial ecosystems, is sensitive to climate changes such as temperature and precipitation changes. A growing body of evidence has indicated that ambient climate affects the temperature sensitivity of SR, which benchmarks the strength of terrestrial soil carbon-climate feedbacks. However, whether SR sensitivity to precipitation changes is influenced by ambient climate is still not clear. In addition, the mechanism driving the above phenomenon is still poorly understood. Here, a long-term field manipulation experiment with five precipitation treatments (-60%, -40%, +0%, +40%, and +60% of annual precipitation) was conducted in a marsh in the Yellow River Delta, China, which is sensitive to soil drying-wetting cycle caused by precipitation changes. Results showed that SR increased exponentially along the experimental precipitation gradient each year and the sensitivity of SR (standardized by per 100 mm change in precipitation under precipitation treatments) exhibited significant interannual variation from 2016 to 2021. In addition, temperature, net radiation, and ambient precipitation all exhibited dramatic interannual variability; however, only ambient precipitation had a significant negative correlation with SR sensitivity. Moreover, the sensitivity of SR was significantly positively related to the sensitivity of belowground biomass (BGB) across 6 years. Structural equation modeling and regression analysis also showed that precipitation treatments significantly affected SR and its autotrophic and heterotrophic components by altering BGB. Our study demonstrated that ambient precipitation determines the sensitivity of SR to precipitation treatments in marshes. The findings underscore the importance of ambient climate in regulating ecosystem responses in long-term field manipulation experiments.

Soil moisture drives the spatiotemporal patterns of asymmetry in vegetation productivity responses across China

Increasingly drastic global change is expected to cause hydroclimatic changes, which will influence vegetation productivity and pose a threat to the terrestrial carbon sink. Asymmetry represents an imbalance between vegetation growth and loss of growth during dry and wet periods, respectively. However, the mechanisms of asymmetric plant responses to hydrological changes remain poorly understood. Here, we examined the spatiotemporal patterns of asymmetric responses of vegetation productivity across terrestrial ecosystems in China. We analyzed several observational and satellite-based datasets of plant productivity and several reanalyzed datasets of hydroclimatic variables from 2001 to 2020, and used a random forest model to assess the importance of hydroclimatic variables for these responses. Our results showed that the productivity of >50 % of China's vegetated areas showed a more positive asymmetry (2.3 ± 9.4 %) over the study period, which were distributed broadly in northwest China (mainly grasslands and sparse vegetation ecosystems). Negative asymmetries were most common in forest ecosystems in northeast China. We demonstrated that one-third of vegetated areas tended to exhibit significant changes in asymmetry during 2001-2020. The trend towards stronger positive asymmetry (0.95 % yr^-1) was higher than that towards stronger negative asymmetry (-0.55 % yr^-1), which is beneficial for the carbon sink. We further showed that in China, soil moisture was a more important driver of spatiotemporal changes in asymmetric productivity than precipitation. We identified thresholds of surface soil moisture (20-30 %, volume water content) and root-zone soil moisture (200-350 mm, equivalent water height) that were associated with changes in asymmetry. Our findings highlight the necessity of considering the dynamic responses of vegetation to hydrological factors in order to fully understand the physiological growth processes of plants and avoid the possible loss of productivity due to future climate change.

Divergent roles of herbivory in eutrophying forests

Ungulate populations are increasing across Europe with important implications for forest plant communities. Concurrently, atmospheric nitrogen (N) deposition continues to eutrophicate forests, threatening many rare, often more nutrient-efficient, plant species. These pressures may critically interact to shape biodiversity as in grassland and tundra systems, yet any potential interactions in forests remain poorly understood. Here, we combined vegetation resurveys from 52 sites across 13 European countries to test how changes in ungulate herbivory and eutrophication drive long-term changes in forest understorey communities. Increases in herbivory were associated with elevated temporal species turnover, however, identities of winner and loser species depended on N levels. Under low levels of N-deposition, herbivory favored threatened and small-ranged species while reducing the proportion of non-native and nutrient-demanding species. Yet all these trends were reversed under high levels of N-deposition. Herbivores also reduced shrub cover, likely exacerbating N effects by increasing light levels in the understorey. Eutrophication levels may therefore determine whether herbivory acts as a catalyst for the "N time bomb" or as a conservation tool in temperate forests.

Using functional indicators to detect state changes in terrestrial ecosystems

Indicators to predict ecosystem state change are urgently needed to cope with the degradation of ecosystem services caused by global change. With the development of new technologies for measuring ecosystem function with fine spatiotemporal resolution over broad areas, we are in the era of 'big data'. However, it is unclear how large, emerging datasets can be used to anticipate ecosystem state change. We propose the construction of indicators based on functional variables (flows) and state variables (pools) to predict future ecosystem state changes. The indicators identified here may be useful signals for doing so. In addition, functional indicators have explicit ecological meanings that can identify the ecological mechanism that is causing state changes, and can thus be used to improve ecosystem models.

Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments

Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.

Global evidence on the asymmetric response of gross primary productivity to interannual precipitation changes

Understanding gross primary productivity (GPP) response to precipitation (PPT) changes is essential for predicting land carbon uptake under increasing PPT variability and extremes. Previous studies found that ecosystem GPP may have an asymmetric response to PPT changes, leading to the inconsistency of GPP gains in wet years compared to GPP declines in dry years. However, it is unclear how the asymmetric responses vary among vegetation types and under different PPT variabilities. This study evaluated the global patterns of asymmetries of GPP response to different PPT changes using two state-of-science global GPP datasets. The result shows that under mild PPT changes (|ΔPPT| ≤ 25%), grasslands, savannas, shrublands, and tundra show positive asymmetric responses (i.e., larger GPP gains in wet years than GPP losses in dry years), while other vegetation types show negative asymmetric responses (i.e., larger GPP losses in dry years than GPP gains in wet years). Conversely, all vegetation types show negative GPP asymmetric responses to moderate (25% < |ΔPPT| ≤ 50%) and extreme (|ΔPPT| > 50%) PPT changes. Thus, we propose a new non-linear asymmetric GPP-PPT model that incorporates three modes with regards to vegetation types. Meanwhile, we found that the spatial patterns of asymmetry were mainly driven by PPT amount and variability. Stronger and negative asymmetries were found in areas with smaller PPT amount and variability, while positive asymmetries were found in areas with higher PPT variability. These findings promote our understanding of carbon dynamics under increased PPT variability and extremes and provide new insights for land models to better predict future carbon uptake and its feedback to climate change.

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts—Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25° × 0.25° spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions.

Precipitation-productivity relationships and the duration of precipitation anomalies: An underappreciated dimension of climate change

In terrestrial ecosystems, climate change forecasts of increased frequencies and magnitudes of wet and dry precipitation anomalies are expected to shift precipitation-net primary productivity (PPT-NPP) relationships from linear to nonlinear. Less understood, however, is how future changes in the duration of PPT anomalies will alter PPT-NPP relationships. A review of the literature shows strong potential for the duration of wet and dry PPT anomalies to impact NPP and to interact with the magnitude of anomalies. Within semi-arid and mesic grassland ecosystems, PPT gradient experiments indicate that short-duration (1 year) PPT anomalies are often insufficient to drive nonlinear aboveground NPP responses. But long-term studies, within desert to forest ecosystems, demonstrate how multi-year PPT anomalies may result in increasing impacts on NPP through time, and thus alter PPT-NPP relationships. We present a conceptual model detailing how NPP responses to PPT anomalies may amplify with the duration of an event, how responses may vary in xeric vs. mesic ecosystems, and how these differences are most likely due to demographic mechanisms. Experiments that can unravel the independent and interactive impacts of the magnitude and duration of wet and dry PPT anomalies are needed, with multi-site long-term PPT gradient experiments particularly well-suited for this task.