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Abstract Over the last few decades, there has been an increasing number of controlled‐manipulative experiments to investigate how plants and soils might respond to global change. These experiments typically examined the effects of each of three global change drivers [i.e., nitrogen (N) deposition, warming, and elevated CO 2 ] on primary productivity and on the biogeochemistry of carbon (C), N, and phosphorus (P) across different terrestrial ecosystems. Here, we capitalize on this large amount of information by performing a comprehensive meta‐analysis (>2000 case studies worldwide) to address how C:N:P stoichiometry of plants, soils, and soil microbial biomass might respond to individual vs. combined effects of the three global change drivers. Our results show that (i) individual effects of N addition and elevated CO 2 on C:N:P stoichiometry are stronger than warming, (ii) combined effects of pairs of global change drivers (e.g., N addition + elevated CO 2 , warming + elevated CO 2 ) on C:N:P stoichiometry were generally weaker than the individual effects of each of these drivers, (iii) additive interactions (i.e., when combined effects are equal to or not significantly different from the sum of individual effects) were more common than synergistic or antagonistic interactions, (iv) C:N:P stoichiometry of soil and soil microbial biomass shows high homeostasis under global change manipulations, and (v) C:N:P responses to global change are strongly affected by ecosystem type, local climate, and experimental conditions. Our study is one of the first to compare individual vs. combined effects of the three global change drivers on terrestrial C:N:P ratios using a large set of data. To further improve our understanding of how ecosystems might respond to future global change, long‐term ecosystem‐scale studies testing multifactor effects on plants and soils are urgently required across different world regions.

Abstract This study investigated seasonal patterns in stoichiometric ratios, nutrient resorption characteristics, and nutrient use strategies of dominant tree species at three successional stages in subtropical China, which have not been fully understood. Fresh leaf and leaf litterfall samples were collected in growing and nongrowing seasons for determining the concentrations of carbon (C), nitrogen (N), and phosphorus (P). Then, stoichiometric ratios (i.e., C:N, C:P, N:P, and C:N:P) and resorption parameters were calculated. Our results found that there was no consistent variation in leaf C:N and C:P ratios among different species. However, leaf N:P ratios in late‐successional species became significantly higher, indicating that P limitation increases during successional development. Due to the P limitation in this study area, P resorption efficiency and proficiency were higher than corresponding N resorption parameters. Dominant tree species at early‐successional stage adopted “conservative consumption” nutrient use strategy, whereas the species at late‐successional stage inclined to adopt “resource spending” strategy.

Abstract Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network ( ANN ) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature ( MAT ), mean annual precipitation ( MAP ), and biome type as inputs ( r 2  = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT ( r 2  = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle. , Key Points Predict biome‐specific global soil respiration from 1960 to 2012 using an artificial neural network model Prediction determined an average global soil respiration of 93.3 ± 6.1 Pg C yr −1 and an increasing trend of 0.04 Pg C yr −1 The 10 biome‐specific soil respiration estimates made it possible to trace different responses to global climate change in each biome

Abstract Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process‐based model TRIPLEX‐GHG was developed by coupling it with the new MEND (Microbial‐ENzyme‐mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX‐MICROBE) shows considerable improvement over the previous version (TRIPLEX‐GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well‐regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. (2014). We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and mineral‐associated organic carbon (MOC). However, our work represents the first step toward a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles. , Key Points Traditional soil carbon models are lacking in their representation of key microbial processes that control the soil carbon response to global climate change A Ecosystem model (TRIPLEX‐MICROBE) offers considerable improvement over a previous version (TRIPLEX‐GHG) in simulating soil organic carbon Our work is the first step toward a new generation of ecosystem process models that integrate key microbial processes into soil carbon cycles

Abstract Methane (CH 4 ) emissions from tropical wetlands contribute 60%–80% of global natural wetland CH 4 emissions. Decreased wetland CH 4 emissions can act as a negative feedback mechanism for future climate warming and vice versa. The impact of the El Niño–Southern Oscillation (ENSO) on CH 4 emissions from wetlands remains poorly quantified at both regional and global scales, and El Niño events are expected to become more severe based on climate models’ projections. We use a process‐based model of global wetland CH 4 emissions to investigate the impacts of the ENSO on CH 4 emissions in tropical wetlands for the period from 1950 to 2012. The results show that CH 4 emissions from tropical wetlands respond strongly to repeated ENSO events, with negative anomalies occurring during El Niño periods and with positive anomalies occurring during La Niña periods. An approximately 8‐month time lag was detected between tropical wetland CH 4 emissions and ENSO events, which was caused by the combined time lag effects of ENSO events on precipitation and temperature over tropical wetlands. The ENSO can explain 49% of interannual variations for tropical wetland CH 4 emissions. Furthermore, relative to neutral years, changes in temperature have much stronger effects on tropical wetland CH 4 emissions than the changes in precipitation during ENSO periods. The occurrence of several El Niño events contributed to a lower decadal mean growth rate in atmospheric CH 4 concentrations throughout the 1980s and 1990s and to stable atmospheric CH 4 concentrations from 1999 to 2006, resulting in negative feedback to global warming.

Abstract Interannual air temperature variability has changed over some regions in Northern Hemisphere (NH), accompanying with climate warming. However, whether and to what extent it regulates the interannual sensitivity of vegetation growth to temperature variability (i.e., interannual temperature sensitivity)—one central issue in understanding and predicting the responses of vegetation growth to changing climate—still remains poorly quantified and understood. Here we quantify the relationships between the interannual temperature sensitivity of mean growing‐season (April–October) normalized difference vegetation index (NDVI) and ecosystem model simulations of gross primary productivity (GPP), and variability in mean growing‐season temperature for forest, shrub, and grass over NH. We find that higher interannual variability in mean growing‐season temperature leads to consistent decrease in interannual temperature sensitivity of mean growing‐season NDVI among all vegetation types but not in model simulations of GPP. Drier condition associates with ~130 ± 150% further decrease in interannual temperature sensitivity of mean growing‐season NDVI by temperature variability in forest and shrub. These results illustrate that varying temperature variability can significantly regulate the interannual temperature sensitivity of vegetation growth over NH, interacted with drought variability and nonlinear responses of photosynthesis to temperature. Our findings call for an improved characterization of the nonlinear effects of temperature variability on vegetation growth within global ecosystem models. , Key Points It shows consistent decrease in temperature sensitivity of vegetation growth by temperature variability for all vegetation types Larger decrease in temperature sensitivity of vegetation growth by temperature variability is found in forest and shrub in dry regions Drier condition adds further decrease in temperature sensitivity of vegetation growth by temperature variability for forest and shrub in dry regions

The NEBIE plot network is a stand-scale, multi-agency research project designed to compare the ecological effects of a range of silvicultural treatments in northern temperate and boreal forest regions of Ontario, Canada. While research on silviculture intensities has been previously conducted, the NEBIE plot network is at a larger scale, and covers a wider range of intensities in a variety of northern temperate and boreal forest types. Details about experimental design, treatment designs and research sites, are presented in a companion paper which is published in this edition of The Forestry Chronicle. The operational scale of treatment plots allow for assessment of a variety of forest values. We used a criteria and indicator approach to organize long-term research studies on the network sites, with the goal of providing scientific findings that would inform forest policy. Pre-treatment, and 2-, 5-, and 10-year post-harvesting data have been collected. These initial data add to existing information on the effects of intensification of silviculture on biological diversity, forest productivity, ecosystem health and vitality, soil and water resources, contribution of enhanced forest management global carbon cycles, and long-term multiple socio-economic benefits of northern forests.

Abstract Increasing atmospheric methane (CH 4 ) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH 4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH 4 emissions from wetlands, the largest natural global CH 4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH 4 emissions increased by 1.2 Tg yr −1 (−0.2–3.5 Tg yr −1 ), tropical emissions decreased by 0.9 Tg yr −1 (−3.2−1.1 Tg yr −1 ), yet globally, emissions remained unchanged at 184 ± 22 Tg yr −1 . Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH 4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH 4 emissions have not contributed significantly to the period of renewed atmospheric CH 4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH 4 emissions, and a decrease in the atmospheric oxidative sink.