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Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted., Mechanisms and consequences of the acclimation of soil respiration to warming are unclear. Here, the authors combine soil respiration, metagenomics, and functional gene results from a 7-year grassland warming experiment to a microbial-enzyme decomposition model, showing functional gene information to lower uncertainty and improve fit.

Shifts in dominance and species reordering can occur in response to global change. However, it is not clear how altered precipitation and disturbance regimes interact to affect species composition and dominance.We explored community‐level diversity and compositional similarity responses, both across and within years, to a manipulated precipitation gradient and annual clipping in a mixed‐grass prairie in Oklahoma, USA. We imposed seven precipitation treatments (five water exclusion levels [−20%, −40%, −60%, −80%, and −100%], water addition [+50%], and control [0% change in precipitation]) year‐round from 2016 to 2018 using fixed interception shelters. These treatments were crossed with annual clipping to mimic hay harvest.We found that community‐level responses were influenced by precipitation across time. For instance, plant evenness was enhanced by extreme drought treatments, while plant richness was marginally promoted under increased precipitation.Clipping promoted species gain resulting in greater richness within each experimental year. Across years, clipping effects further reduced the precipitation effects on community‐level responses (richness and evenness) at both extreme drought and added precipitation treatments. Synthesis: Our results highlight the importance of studying interactive drivers of change both within versus across time. For instance, clipping attenuated community‐level responses to a gradient in precipitation, suggesting that management could buffer community‐level responses to drought. However, precipitation effects were mild and likely to accentuate over time to produce further community change., Shifts in dominance and species reordering can occur in response to global change. We manipulated an experimental precipitation gradient combined with clipping in a temperate mixed‐grass prairie to examine how both treatments can shape plant communities. We found that initial shifts in abundance were detected by examining species‐ to community‐level changes over time; drought increased evenness and this was related to the decline of the dominant species and increase in subdominants, while mesic conditions mildly promoted plant richness.

Phosphorus limitation on terrestrial plant growth is being incorporated into Earth system models. The global pattern of terrestrial phosphorus limitation, however, remains unstudied. Here, we examined the global-scale latitudinal pattern of terrestrial phosphorus limitation by analysing a total of 1068 observations of aboveground plant production response to phosphorus additions at 351 forest, grassland or tundra sites that are distributed globally. The observed phosphorus-addition effect varied greatly (either positive or negative), depending significantly upon fertilisation regime and production measure, but did not change significantly with latitude. In contrast, phosphorus-addition effect standardised by fertilisation regime and production measure was consistently positive and decreased significantly with latitude. Latitudinal gradient in the standardised phosphorus-addition effect was explained by several mechanisms involving substrate age, climate, vegetation type, edaphic properties and biochemical machinery. This study suggests that latitudinal pattern of terrestrial phosphorus limitation is jointly shaped by macro-scale driving forces and the fundamental structure of life.

Nitrogen immobilization usually leads to nitrogen retention in soil and, thus, influences soil nitrogen supply for plant growth. Understanding soil nitrogen immobilization is important for predicting soil nitrogen cycling under anthropogenic activities and climate changes. However, the global patterns and drivers of soil nitrogen immobilization remain unclear. We synthesized 1350 observations of gross soil nitrogen immobilization rate (NIR) from 97 articles to identify patterns and drivers of NIR. The global mean NIR was 8.77 ± 1.01 mg N kg-1 soil day-1 . It was 5.55 ± 0.41 mg N kg-1 soil day-1 in croplands, 15.74 ± 3.02 mg N kg-1 soil day-1 in wetlands, and 15.26 ± 2.98 mg N kg-1 soil day-1 in forests. The NIR increased with mean annual temperature, precipitation, soil moisture, soil organic carbon, total nitrogen, dissolved organic nitrogen, ammonium, nitrate, phosphorus, and microbial biomass carbon. But it decreased with soil pH. The results of structural equation models showed that soil microbial biomass carbon was a pivotal driver of NIR, because temperature, total soil nitrogen, and soil pH mostly indirectly influenced NIR via changing soil microbial biomass. Moreover, microbial biomass carbon accounted for most of the variations in NIR among all direct relationships. Furthermore, the efficiency of transforming the immobilized nitrogen to microbial biomass nitrogen was lower in croplands than in natural ecosystems (i.e., forests, grasslands, and wetlands). These findings suggested that soil nitrogen retention may decrease under the land use change from forests or wetlands to croplands, but NIR was expected to increase due to increased microbial biomass under global warming. The identified patterns and drivers of soil nitrogen immobilization in this study are crucial to project the changes in soil nitrogen retention.

Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings (R2 = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.

With the development and popularization of online shopping, a large number of commodities on the online retail platform should be managed through the multilevel category system, in which there are thousands of categories. Automatic commodity classification has become an issue. Classifying commodities according to their text titles can improve the efficiency of online retail platforms. The text titles of commodities contain the information of commodities, but the text titles seldom follow the grammar rules and the text length varies greatly. We propose a classification model, Temporal Multiple-Convolutional Network (TMN), which combines Temporal Convolutional Network(TCN) model and Multiple-Convolutional Neural Network(MCNN) model. The TCN model is firstly used to model sequence data based on both word embedding and character embedding respectively, and generate two output vectors. Then, the MCNN models are used to extract features from the abstract vectors. Finally, output layer employs softmax function to classify commodities. We conduct experiment on the online retail platform dataset provided by Inspur Group Co., Ltd. The results show that the TMN has a commodity classification accuracy of 84.2%, which is superior to that of state-of-the-art models.

Phosphorus (P) limitation of aboveground plant production is usually assumed to occur in tropical regions but rarely elsewhere. Here we report that such P limitation is more widespread and much stronger than previously estimated. In our global meta-analysis, almost half (46.2%) of 652 P-addition field experiments reveal a significant P limitation on aboveground plant production. Globally, P additions increase aboveground plant production by 34.9% in natural terrestrial ecosystems, which is 7.0–15.9% higher than previously suggested. In croplands, by contrast, P additions increase aboveground plant production by only 13.9%, probably because of historical fertilizations. The magnitude of P limitation also differs among climate zones and regions, and is driven by climate, ecosystem properties, and fertilization regimes. In addition to confirming that P limitation is widespread in tropical regions, our study demonstrates that P limitation often occurs in other regions. This suggests that previous studies have underestimated the importance of altered P supply on aboveground plant production in natural terrestrial ecosystems., Plants are thought to be limited by phosphorus (P) especially in tropical regions. Here, Hou et al. report a meta-analysis of P fertilization experiments to show widespread P limitation on plant growth across terrestrial ecosystems modulated by climate, ecosystem properties, and fertilization regimes

Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in the literature, including a commonly used turnover time. However, we know little about how different transit time and turnover time are in representing carbon cycling through multiple compartments under a non-steady state. In this study, we estimate both C turnover time as defined by the conventional stock over flux and mean C transit time as defined by the mean age of C mass leaving the system. We incorporate them into the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate C turnover time and transit time in response to climate warming and rising atmospheric [CO2]. Modelling analysis shows that both C turnover time and transit time increase with climate warming but decrease with rising atmospheric [CO2]. Warming increases C turnover time by 2.4 years and transit time by 11.8 years in 2100 relative to that at steady state in 1901. During the same period, rising atmospheric [CO2] decreases C turnover time by 3.8 years and transit time by 5.5 years. Our analysis shows that 65 % of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35 % increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO2] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the transit time, the turnover time, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of warming and rising atmospheric [CO2] on C diagnostic time and potentially led to deviations in estimating land C sequestration in multi-compartmental ecosystems.

Ubiquitous thermal acclimation of leaf respiration could mitigate the respiration increase. However, whether species of different plant functional groups showing distinct or similar acclimation justifies the simple prediction of respiratory carbon (C) loss to a warming climate. In this study, leaf dark respiration (Rd) of illinois bundleflower (IB, legume), stiff goldenrod (GR, C₃ forbs), indian grass, little bluestem and king ranch bluestem (IG, LB and KB, C₄ grass) were measured with detached leaves sampled in a 17-year warming experiment. The results showed that Rd at 20°C and 22°C (R₂₀ and R₂₂) were significantly lower in the warming treatment for all the five species. Lower R₂₂ in warmed than R₂₀ in control in GR, KB, LB and IG imply acclimation homeostasis, but not in IB. The significant decline in temperature sensitivity of respiration (Q₁₀) of GR resulted in the marginal reduction of Q₁₀ across species. No significant changes in Q₁₀ of C₄ grasses suggest different acclimation types for C₃ forbs and C₄ grass. The magnitude of acclimation positively correlated with leaf C/N. Our results suggest that non-legume species had a relative high acclimation, although the acclimation type was different between C₃ forbs and C₄ grasses, and the legume species displayed no acclimation in Rd. Thus, the plant functional types should be taken into account in the grassland ecosystem C models.

Succession, a central theme throughout the history of ecology, has been well studied predominantly in plant communities, but the general trends in soil microbial communities during succession remain unclear. Here, we compiled a comprehensive data set of 85 age sequences worldwide with the aims to (1) examine the trends in soil microbial composition, bioenergetics, and activity during secondary succession, and (2) explore their coordinating changes with soil properties. The results showed that the fungi to bacteria ratio ( fungi:bacteria ) increased, while the microbial respiration per unit biomass carbon ( R/C mic ) decreased as the succession proceeds. Secondary succession had the rising trends in microbial biomass carbon to soil carbon ratio ( C mic / C soil ) and microbial biomass nitrogen to soil nitrogen ratio ( N mic / N soil ). These successional trends in microbes were coincident with the macro-ecological succession theory in plants and animals. Specifically, early successional stages tended to be dominated by r -strategists (bacteria) that had higher R/C mic and lower C mic / C soil and N mic / N soil , whereas late successional stages tended to be dominated by K -strategists (fungi) that behaved oppositely. The soil C to N ratio ( C:N soil ) increased significantly with the successional stage, with a fast increasing C:N soil ratio being accompanied by a fast increase of fungi:bacteria , a slow decrease of R/C mic , and a slow increase of C mic /C soil . This result suggests that the stoichiometry theory may provide a feasible approach to explain the divergent successional trends in microbial communities. In conclusion, our global synthesis highlights the application of the existing macro-ecological theory to soil microbial ecology studies.

Anthropogenic nitrogen (N) deposition is expected to increase substantially and continuously in the future. Soil N availability regulates microbial communities and the decomposition and formation of soil organic matter, which have great impacts on global carbon (C) cycling. We conducted a meta-analysis based on 454 N-addition experiments in order to synthesize the patterns and mechanisms of responses by soil microbial communities to N addition in various biomes (i.e., boreal forest, temperate forest, tropical/subtropical forest, grassland, and desert). Results showed that the effects of N addition on the total microbial biomass varied depending on biome types, methodologies (fumigation–extraction technique vs. total phospholipid fatty acid), and N-addition rates. Nitrogen addition consistently decreased the microbial C:N and fungi to bacteria ratio (F:B), but increased Gram positive bacteria to Gram negative bacteria ratio (GP:GN) among biome types and N-addition rates. Nitrogen addition increased soil N availability and thereby resulted in soil acidification. Regression technique and principal component analyses showed that the shifts in the F:B and GP:GN mainly resulted from enhanced N availability due to N addition rather than soil acidification. When the N addition rate is lower than 100 kg N ha−1 year−1, about ten times higher than of global normal rate, the positive response of microbial growth was found. Overall, these findings revised the previous notion that N addition inhibited the microbial growth. Microbial species shifts might accentuate or mitigate the effects of alterations in microbial biomass at the ecosystem level, highlighting the critical role of microbial community composition in soil ecosystem functions under N deposition scenarios.

Uptake of anthropogenically emitted carbon (C) dioxide by terrestrial ecosystem is critical for determining future climate. However, Earth system models project large uncertainties in future C storage. To help identify sources of uncertainties in model predictions, this study develops a transient traceability framework to trace components of C storage dynamics. Transient C storage (X) can be decomposed into two components, C storage capacity (Xc) and C storage potential (Xp). Xc is the maximum C amount that an ecosystem can potentially store and Xp represents the internal capacity of an ecosystem to equilibrate C input and output for a network of pools. Xc is co-determined by net primary production (NPP) and residence time (τN), with the latter being determined by allocation coefficients, transfer coefficients, environmental scalar, and exit rate. Xp is the product of redistribution matrix (τch) and net ecosystem exchange. We applied this framework to two contrasting ecosystems, Duke Forest and Harvard Forest with an ecosystem model. This framework helps identify the mechanisms underlying the responses of carbon cycling in the two forests to climate change. The temporal trajectories of X are similar between the two ecosystems. Using this framework, we found that different mechanisms leading to a similar trajectory between the two ecosystems. This framework has potential to reveal mechanisms behind transient C storage in response to various global change factors. It can also identify sources of uncertainties in predicted transient C storage across models and can therefore be useful for model intercomparison.

Ericoid mycorrhizal associations are a symbiotic relationship between soil fungi and ericaceous plants. Diversity of fungi associated with hair roots of ericaceous plants may vary as a result of frequent disturbances by human activities. The fungal diversity and communities associated with hair roots of Rhododendron ovatum were investigated along a human disturbance gradient in subtropical forests of China. Nine hundred fungal operational taxonomic units were determined by high-throughput sequencing, including different phyla such as Ascomycota, Basidiomycota, Zygomycota, Chytridiomycota, and Glomeromycota. The dominant phylum in Cunninghamia lanceolata plantations and old-growth forest was Ascomycota, while Basidiomycota was the dominant phylum in secondary forests. The indicator species analyses showed that more pathogenic indicator fungi appeared in the disturbed forests, whereas more putative ericoid mycorrhizal fungi existed in the old-growth forests. Principal component analysis also showed that the fungal communities in the hair roots of R. ovatum were distinct between natural forests and plantations, suggesting that the fungal communities associated with hair roots of R. ovatum after logging were resilient and could recover to predisturbance status. The results of envfit analysis showed that performance of host plants rather than accompanying plant community and soil parameters of plots was the key determinant of the root-associated fungal community of R. ovatum.

©2017. American Geophysical Union. All Rights Reserved. Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

With the development and popularization of smart-phones, human activity recognition methods based on contact perception are proposed. The smartphones which are embedded with various sensors can be used as a platform of mobile sensing for human activity recognition. In this paper, we propose an automated human activity recognition network HDL with smartphone motion sensor units. The HDL network combines DBLSTM (Deep Bidirectional Long Short-Term Memory) model and CNN (Convolutional neural network) model. The DBLSTM model is first used to model long sequence data and ultimately generate a bidirectional output vector in a abstract way. The DBLSTM model is good at dealing with serialization tasks but poor in the ability to extract features. Hence, the CNN model is then used to extract features from the abstract vector. Finally, the output layer employs a softmax function to classify human activities. We conduct experiments on the Public domain UCI dataset. The experimental results show that the proposed HDL network achieves reliable results with accuracy and F1 score as high as 97.95% and 97.27%. Compared with other networks based on the same smartphone dataset, the accuracy of HDL is higher than S-LSTM and Dropout CNN network by 2.14% and 6.97% respectively.

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO 2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO 2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO 2 uptake since late 1990s. Here, based on in situ atmospheric CO 2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO 2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO 2 concentration ([CO 2 ] min ), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO 2 concentration.

Despite the importance of future carbon (C) pools for policy and land management decisions under various climate change scenarios, predictions of these pools under altered climate vary considerably. Chronic warming will likely impact both ecosystem C fluxes and the abundance and distribution of plant functional types (PFTs) within systems, potentially interacting to create novel patterns of C exchange. Here, we report results from a 3-year warming experiment using open top chambers (OTC) on the Tibetan Plateau meadow grassland. Warming significantly increased C uptake through gross primary productivity (GPP) but not ecosystem respiration (ER), resulting in a 31.0% reduction in net ecosystem exchange (NEE) in warmed plots. The OTC-induced changes in ecosystem C fluxes were not fully explained by the corresponding changes in soil temperature and moisture. Warming treatments significantly increased the biomass of graminoids and legumes by 12.9 and 27.6%. These functional shifts were correlated with enhanced local GPP, but not ER, resulting in more negative NEE in plots with larger increases in graminoid and legume biomass. This may be due to a link between greater legume abundance and higher levels of total inorganic nitrogen, which can potentially drive higher GPP, but not higher ER. Overall, our results indicate that C-climate feedbacks might be closely mediated by climate-induced changes in PFTs. This highlights the need to consider the impacts of changes in PFTs when predicting future responses of C pools under altered climate scenarios.

Global warming is anticipated to have profound effects on terrestrial carbon fluxes and thus feed backs to future climate change. Ecosystem respiration (Reco) is one of the dominant components of biosphere CO2 fluxes, but the effects of warming on Reco are still unclear. A field warming experiment using open top chambers (OTCs) was conducted in a meadow grassland on the Tibetan Plateau to study the effects of warming on the components of Reco. Warming significantly enhanced above-ground plant respiration (Ragb) and total autotrophic plant respiration (Rplant) by 28.7% and 19.9%, respectively, but reduced heterotrophic respiration (Rh) by 10.4%. These different responses resulted in the insensitive responses of Reco and soil respiration (Rs) to the experimental warming. The warming treatment also increased Rplant/Reco and Ragb/Reco by 8.4% and 17.3%, respectively, while decreasing Rh/Reco by 19.0%, suggesting that warming could eventually cause Reco to be dominated by Rplant. Enhancements in Rplant and Ragb were related to the warming-induced increases in aboveground biomass (AGB) while reduced Rh was closely coupled with warming-induced decrease of microbial biomass carbon. Our results highlight that the differential responses of the components of Reco to different environmental physics under warming scenarios should be taken into consideration to project the future carbon-climate feed backs.

Responses of grassland carbon (C) cycling to climate change and land use remain a major uncertainty in model prediction of future climate. To explore the impacts of global change on ecosystem C fluxes and the consequent changes in C storage, we have conducted a field experiment with warming (+3 °C), altered precipitation (doubled and halved), and annual clipping at the end of growing seasons in a mixed-grass prairie in Oklahoma, USA, from 2009 to 2013. Results showed that although ecosystem respiration (ER) and gross primary production (GPP) negatively responded to warming, net ecosystem exchange of CO2 (NEE) did not significantly change under warming. Doubled precipitation stimulated and halved precipitation suppressed ER and GPP equivalently, with the net outcome being unchanged in NEE. These results indicate that warming and altered precipitation do not necessarily have profound impacts on ecosystem C storage. In addition, we found that clipping enhanced NEE due to a stronger positive response of GPP compared to ER, indicating that clipping could potentially be an effective land practice that could increase C storage. No significant interactions between warming, altered precipitation, and clipping were observed. Meanwhile, we found that belowground net primary production (BNPP) in general was sensitive to climate change and land use though no significant changes were found in NPP across treatments. Moreover, negative correlations of the ER/GPP ratio with soil temperature and moisture did not differ across treatments, highlighting the roles of abiotic factors in mediating ecosystem C fluxes in this grassland. Importantly, our results suggest that belowground C cycling (e.g., BNPP) could respond to climate change with no alterations in ecosystem C storage in the same period.

Unique soil properties in rhizosphere can affect plant growth and biogeochemical cycles of ecosystems. While rhizosphere has been widely investigated, little is known about differences in the rhizosphere effect (RE) between co-existing overstory trees and understory shrubs and herbs in forest ecosystems. In this study, we investigated REs on soil chemical properties of overstory trees and understory shrubs and herbs in forest plantations of southern China. Bulk soil and rhizospheres were sampled in April, July and December 2017. Soil pH, nitrate and ammonium nitrogen, dissolved organic carbon, available phosphorus, total carbon, total nitrogen, and total phosphorus were tested. The REs were defined as the ratios of the chemical properties of the rhizospheres to those of the bulk soil. Our results showed that pH was lower and nutrient contents were higher in the plant rhizospheres than the bulk soil. REs were generally larger in trees than understory plants. The REs were larger in July than April and December. Our findings indicated that the RE varied among plant life forms, species and sampling times, emphasizing the functional role of the RE of understory vegetation in subtropical forests.

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87g/m2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3 -dominant plant community to a perennial C4 -dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from -17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change.

Quantifying the ecological patterns of loss of ecosystem function in extreme drought is important to understand the carbon exchange between the land and atmosphere. Rain-use efficiency [RUE; gross primary production (GPP)/precipitation] acts as a typical indicator of ecosystem function. In this study, a novel method based on maximum rain-use efficiency (RUEmax) was developed to detect losses of ecosystem function globally. Three global GPP datasets from the MODIS remote sensing data (MOD17), ground upscaling FLUXNET observations (MPI-BGC), and process-based model simulations (BESS), and a global gridded precipitation product (CRU) were used to develop annual global RUE datasets for 2001–2011. Large, well-known extreme drought events were detected, e.g. 2003 drought in Europe, 2002 and 2011 drought in the U.S., and 2010 drought in Russia. Our results show that extreme drought-induced loss of ecosystem function could impact 0.9% ± 0.1% of earth's vegetated land per year and was mainly distributed in semi-arid regions. The reduced carbon uptake caused by functional loss (0.14 ± 0.03 PgC/yr) could explain >70% of the interannual variation in GPP in drought-affected areas (p ≤ 0.001). Our results highlight the impact of ecosystem function loss in semi-arid regions with increasing precipitation variability and dry land expansion expected in the future.

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitation changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. We used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. This highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes. Fil: Wilcox, Kevin R.. University of Oklahoma; Estados Unidos Fil: Shi, Zheng. University of Oklahoma; Estados Unidos Fil: Gherardi, Laureano. Arizona State University; Estados Unidos Fil: Lemoine, Nathan P.. State University of Colorado - Fort Collins; Estados Unidos Fil: Koerner, Sally E.. University of South Florida; Estados Unidos Fil: Hoover, David L.. United States Department of Agriculture. Agricultural Research Service; Argentina Fil: Bork, Edward. University of Alberta; Canadá Fil: Byrne, Kerry M.. Humboldt State University; Estados Unidos Fil: Cahill Jr., James. University of Alberta; Canadá Fil: Collins, Scott L.. University of New Mexico; Estados Unidos Fil: Evans, Sarah. Michigan State University; Estados Unidos Fil: Gilgen, Anna K.. Swiss Federal Institute of Technology Zurich; Suiza Fil: Holub, Petr. Czech Academy of Sciences; República Checa Fil: Jiang, Lifen. University of Oklahoma; Estados Unidos Fil: Knapp, Alan K.. State University of Colorado - Fort Collins; Estados Unidos Fil: LeCain, Daniel. United States Department of Agriculture. Agricultural Research Service; Argentina Fil: Liang, Junyi. University of Oklahoma; Estados Unidos Fil: García Palacios, Pablo. Universidad Rey Juan Carlos; España Fil: Peñuelas, Josep. Consejo Superior de Investigaciones Científicas. Centre de Recerca Ecológica I Aplicacions Forestals; España. Universitat Autònoma de Barcelona; España Fil: Pockman, William T.. University of New Mexico; Estados Unidos Fil: Smith, Melinda D.. State University of Colorado - Fort Collins; Estados Unidos Fil: Sun, Shanghua. Northwest A & F University; China Fil: White, Shannon R.. Government of Alberta; Canadá Fil: Yahdjian, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. ; Argentina Fil: Zhu, Kai. Rice University; Estados Unidos. University of Texas; Estados Unidos Fil: Luo, Yiqi. University of Oklahoma; Estados Unidos

Past global change studies have identified changes in species diversity as a major mechanism regulating temporal stability of production, measured as the ratio of the mean to the standard deviation of community biomass. However, the dominant plant functional group can also strongly determine the temporal stability. Here, in a grassland ecosystem subject to 15 years of experimental warming and hay harvest, we reveal that warming increases while hay harvest decreases temporal stability. This corresponds with the biomass of the dominant C4 functional group being higher under warming and lower under hay harvest. As a secondary mechanism, biodiversity also explains part of the variation in temporal stability of production. Structural equation modelling further shows that warming and hay harvest regulate temporal stability through influencing both temporal mean and variation of production. Our findings demonstrate the joint roles that dominant plant functional group and biodiversity play in regulating the temporal stability of an ecosystem under global change., Species diversity is thought to play an important role in maintaining production stability. Shi et al. demonstrate that the dominant C4 plant also makes a substantial contribution to temporal stability in a grassland ecosystem subject to 15 years of experimental warming and hay harvest.

© 2016, National Academy of Sciences. All rights reserved. The respiratory release of carbon dioxide (CO2) from soil is a major yet poorly understood flux in the global carbon cycle. Climatic warming is hypothesized to increase rates of soil respiration, potentially fueling further increases in global temperatures. However, despite considerable scientific attention in recent decades, the overall response of soil respiration to anticipated climatic warming remains unclear. We synthesize the largest global dataset to date of soil respiration, moisture, and temperature measurements, totaling >3,800 observations representing 27 temperature manipulation studies, spanning nine biomes and over 2 decades of warming. Our analysis reveals no significant differences in the temperature sensitivity of soil respiration between control and warmed plots in all biomes, with the exception of deserts and boreal forests. Thus, our data provide limited evidence of acclimation of soil respiration to experimental warming in several major biome types, contrary to the results from multiple single-site studies. Moreover, across all nondesert biomes, respiration rates with and without experimental warming follow a Gaussian response, increasing with soil temperature up to a threshold of ∼25 °C, above which respiration rates decrease with further increases in temperature. This consistent decrease in temperature sensitivity at higher temperatures demonstrates that rising global temperatures may result in regionally variable responses in soil respiration, with colder climates being considerably more responsive to increased ambient temperatures compared with warmer regions. Our analysis adds a unique cross-biome perspective on the temperature response of soil respiration, information critical to improving our mechanistic understanding of how soil carbon dynamics change with climatic warming.

©2015. American Geophysical Union. All Rights Reserved. Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

Ericoid mycorrhiza (ERM) are expected to facilitate establishment of ericaceous plants in harsh habitats. However, diversity and driving factors of the root-associated fungi of ericaceous plants are poorly understood. In this study, hair-root samples of Vaccinium carlesii were taken from four forest types: old growth forests (OGF), secondary forests with once or twice cutting (SEC I and SEC II) and Cunninghamia lanceolata plantation (PLF). Fungal communities were determined using high-throughput sequencing and impacts of human disturbances and the intra- and inter-annual variability of root-associated fungal community were evaluated. Diverse fungal taxa were observed and our results showed that (1) Intra- and inter-annual changes in root-associated fungal community were found and the Basidiomycota to Ascomycota ratio was related to mean temperature of the sampling month; (2) Human disturbances significantly affected structure of root-associated fungal community of V. carlesii and two secondary forest types were similar in root-associated fungal community and were closer to that of the old growth forest; (3) Plant community composition, edaphic parameters and geographic factors significantly affected root-associated fungal communities of V. carlesii. These results may be helpful in better understanding the maintenance mechanisms of fungal diversity associated with hair roots of ERM plants under human disturbances.

Soil microbes play critical roles in regulating terrestrial carbon (C) cycle and its feedback to climate change. However, it is still unclear how the soil microbial community and abundance respond to future climate change scenarios. In this meta-analysis, we synthesized the responses of microbial community and abundance to experimental warming from 64 published field studies. Our results showed that warming significantly increased soil microbial abundance by 7.6% on average. When grouped by vegetation or soil types, tundras and histosols had the strongest microbial responses to warming with increased microbial, fungal and bacterial abundances by 15.0%, 9.5% and 37.0% in tundra and 16.5%, 13.2% and 13.3% in histosols, respectively. We found significant negative relationships of the response ratios of microbial, fungal and bacterial abundances with the mean annual temperature, indicating that warming had stronger effects in colder than warmer regions. Moreover, the response ratios of microbial abundance to warming were positively correlated with those of soil respiration. Our findings therefore indicate that the large quantities of C stored in colder regions are likely to be more vulnerable to climate warming than the soil C stored in other warmer regions.

© 2016 Macmillan Publishers Limited. All rights reserved. The majority of the Earth's terrestrial carbon is stored in the soil. If anthropogenic warming stimulates the loss of this carbon to the atmosphere, it could drive further planetary warming. Despite evidence that warming enhances carbon fluxes to and from the soil, the net global balance between these responses remains uncertain. Here we present a comprehensive analysis of warming-induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia. We find that the effects of warming are contingent on the size of the initial soil carbon stock, with considerable losses occurring in high-latitude areas. By extrapolating this empirical relationship to the global scale, we provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections. Our empirical relationship suggests that global soil carbon stocks in the upper soil horizons will fall by 30 ± 30 petagrams of carbon to 203 ± 161 petagrams of carbon under one degree of warming, depending on the rate at which the effects of warming are realized. Under the conservative assumption that the response of soil carbon to warming occurs within a year, a business-as-usual climate scenario would drive the loss of 55 ± 50 petagrams of carbon from the upper soil horizons by 2050. This value is around 12-17 per cent of the expected anthropogenic emissions over this period. Despite the considerable uncertainty in our estimates, the direction of the global soil carbon response is consistent across all scenarios. This provides strong empirical support for the idea that rising temperatures will stimulate the net loss of soil carbon to the atmosphere, driving a positive land carbon-climate feedback that could accelerate climate change.