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Abstract Natural vegetation restoration can enhance soil organic carbon (SOC) sequestration, but the mechanisms and control factors underlying SOC sequestration are still unknown. The objectives of the study are to quantify the temporal variation of soil and aggregate‐associated organic carbon (OC) and identify factors controlling the variation following natural vegetation restoration after farmland abandonment. We collected soils from sites having 5, 30, 60, 100, and 160 years of a natural vegetation restoration chronosequence after farmland abandonment in the Loess Plateau, China. The results showed that natural vegetation restoration increased macroaggregates (0.25–2 mm; 46.6% to 73.9%), SOC (2.27 to 9.81 g kg −1 ), and aggregate OC (7.33 to 36.98 g kg −1 ) in the top 20‐cm soil compared with abandoned farmland, and the increases mainly occurred in the early stage (<60 years). The increase of SOC was contributed by OC accumulated in macroaggregates (0.25–2 mm) rather than microaggregates (≤0.25 mm). Moreover, SOC sequestration in the topsoil (0–10 cm) was mainly determined by fine root biomass (FR), labile organic carbon (LOC), and microbial biomass carbon (MBC). And in the subsoil (10–20 cm), SOC sequestration was mainly determined by the proportion of macroaggregates. The results suggest that natural vegetation restoration increased SOC and aggregate OC, and FR, MBC, LOC, and the physical protection of aggregates played important roles in regulating SOC and aggregate OC.

Methane (CH4) is a vital greenhouse gas with a 28-fold higher global warming potential than carbon dioxide when considering a molar basis for the time horizon of 100 years. Here, we investigated the variation of soil CH4 fluxes, soil physiochemical properties, and CH4-related bacteria community composition of two forests in China. We measured CH4 fluxes using static chambers and analyzed soil bacterial communities using next-generation high-throughput sequencing in a temperate broad-leaved deciduous forest at Baotianman Nature Reserve (TBDF-BTM) and a tropical rainforest at Jianfengling National Natural Reserve (TRF-JFL). Our results showed that the soils from both sites were CH4 sinks. Significant variation in soil CH4 fluxes was found at TBDF-BTM exclusively, while no seasonal variation in the CH4 uptake was observed at TRF-JFL. The CH4 fluxes at TBDF-BTM were substantially higher than those at TRF-JFL during all seasons. One genus of methanotrophs and three genera of methylotrophs were detected at both sites, though they had no direct relationship with soil CH4 fluxes. Water-filled pore space and soil total carbon content are the main factors controlling the soil CH4 fluxes at TBDF-BTM. At TRF-JFL, the soil CH4 fluxes showed no significant correlations with any of the soil properties. This study improves our understanding of soil CH4 fluxes and their influencing factors in forests in different climatic zones and provides a reference for future investigation of forest soil CH4 fluxes, the forest ecosystem carbon cycle, and the forest CH4 model.

This study presents a critical analysis regarding the assumption of carbon neutrality in life cycle assessment (LCA) models that assess climate change impacts of bioenergy usage. We identified a complex of problems in the carbon neutrality assumption, especially regarding bioenergy derived from forest residues. In this study, we summarized several issues related to carbon neutral assumptions, with particular emphasis on possible carbon accounting errors at the product level. We analyzed errors in estimating emissions in the supply chain, direct and indirect emissions due to forest residue extraction, biogenic CO 2 emission from biomass combustion for energy, and other effects related to forest residue extraction. Various modeling approaches are discussed in detail. We concluded that there is a need to correct accounting errors when estimating climate change impacts and proposed possible remedies. To accurately assess climate change impacts of bioenergy use, greater efforts are required to improve forest carbon cycle modeling, especially to identify and correct pitfalls associated with LCA accounting, forest residue extraction effects on forest fire risk and biodiversity. Uncertainties in accounting carbon emissions in LCA are also highlighted, and associated risks are discussed.

Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants ( E It ) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies. , Key Points Methane emission from wastewater from 2000 to 2014 was calculated to increase from 1349.01 Gg to 3430.03 Gg. Methane emission from wastewater from 2015 to 2020 was estimated to increase from 3875.30 Gg to 5212.75 Gg. A spatial transition of methane emission from wastewater was found and discussed in the present study.

Machine learning, an important branch of artificial intelligence, is increasingly being applied in sciences such as forest ecology. Here, we review and discuss three commonly used methods of machine learning (ML) including decision-tree learning, artificial neural network, and support vector machine and their applications in four different aspects of forest ecology over the last decade. These applications include: (i) species distribution models, (ii) carbon cycles, (iii) hazard assessment and prediction, and (iv) other applications in forest management. Although ML approaches are useful for classification, modeling, and prediction in forest ecology research, further expansion of ML technologies is limited by the lack of suitable data and the relatively “higher threshold” of applications. However, the combined use of multiple algorithms and improved communication and cooperation between ecological researchers and ML developers still present major challenges and tasks for the betterment of future ecological research. We suggest that future applications of ML in ecology will become an increasingly attractive tool for ecologists in the face of “big data” and that ecologists will gain access to more types of data such as sound and video in the near future, possibly opening new avenues of research in forest ecology.

The spruce budworm (SBW) defoliates and kills conifer trees, consequently affecting carbon (C) exchanges between the land and atmosphere. Here, we developed a new TRIPLEX-Insect sub-model to quantify the impacts of insect outbreaks on forest C fluxes. We modeled annual defoliation (AD), cumulative defoliation (CD), and tree mortality. The model was validated against observed and published data at the stand level in the North Shore region of Québec and Cape Breton Island in Nova Scotia, Canada. The results suggest that TRIPLEX-Insect performs very well in capturing tree mortality following SBW outbreaks and slightly underestimates current annual volume increment (CAI). In both mature and immature forests, the simulation model suggests a larger reduction in gross primary productivity (GPP) than in autotrophic respiration (Ra) at the same defoliation level when tree mortality was low. After an SBW outbreak, the growth release of surviving trees contributes to the recovery of annual net ecosystem productivity (NEP) based on forest age if mortality is not excessive. Overall, the TRIPLEX-Insect model is capable of simulating C dynamics of balsam fir following SBW disturbances and can be used as an efficient tool in forest insect management.

Climate change is likely to lead to an increased frequency of droughts and floods, both of which are implicated in large-scale carbon allocation and tree mortality worldwide. Non-structural carbohydrates (NSCs) play an important role in tree survival under stress, but how NSC allocation changes in response to drought or waterlogging is still unclear. We measured soluble sugars (SS) and starch in leaves, twigs, stems and roots of Robinia pseudoacacia L. seedlings that had been subjected to a gradient in soil water availability from extreme drought to waterlogged conditions for a period of 30 days. Starch concentrations decreased and SS concentrations increased in tissues of R. pseudoacacia seedlings, such that the ratio of SS to starch showed a progressive increase under both drought and waterlogging stress. The strength of the response is asymmetric, with the largest increase occurring under extreme drought. While the increase in SS concentration in response to extreme drought is the largest in roots, the increase in the ratio of SS to starch is the largest in leaves. Individual components of SS showed different responses to drought and waterlogging across tissues: glucose concentrations increased significantly with drought in all tissues but showed little response to waterlogging in twigs and stems; sucrose and fructose concentrations showed marked increases in leaves and roots in response to drought but a greater response to drought and waterlogging in stems and twigs. These changes are broadly compatible with the roles of individual SS under conditions of water stress. While it is important to consider the role of NSC in buffering trees against mortality under stress, modelling this behaviour is unlikely to be successful unless it accounts for different responses within organs and the type of stress involved.

Drought has been one of the most important limiting factors for crop production, which deleteriously affects food security worldwide. The main objective of the present study was to quantitatively assess the effect of drought on the agronomic traits (e.g., plant height, biomass, yield, and yield components) of rice and wheat in combination with several moderators (e.g., drought stress intensity, rooting environment, and growth stage) using a meta-analysis study. The database was created from 55 published studies on rice and 60 published studies on wheat. The results demonstrated that drought decreased the agronomic traits differently between rice and wheat among varying growth stages. Wheat and rice yields decreased by 27.5% and 25.4%, respectively. Wheat grown in pots showed greater decreases in agronomic traits than those grown in the field. Rice showed opposite growing patterns when compared to wheat in rooting environments. The effect of drought on rice increased with plant growth and drought had larger detrimental influences during the reproductive phase (e.g., blooming stage, filling stage, and maturity). However, an exception was found in wheat, which had similar decreased performance during the complete growth cycle. Based on these results, future droughts could produce lower yields of rice and wheat when compared to the current drought.

Abstract Wetlands play a critical role in mitigating carbon emission. However, little is known about soil carbon emission and their environmental controls from inland floodplain wetlands. This study aimed to determine the effects of hydrologic and edaphic controllers (water table depth [ WTD ], soil temperature [ Ts ], and soil water content [ SWC ]) on soil C emission in Dongting Lake wetland, China. One‐year emissions were measured in Carex meadow and mudflat using static chambers during 2013 to 2014, including nonflooded season ( NFs ) and flooded season ( Fs ). The results showed that soil C emission in the Carex meadow and mudflat was 307.8 and 264.3 g C·m −2 ·year −1 , respectively, and 50–66% of soil C were emitted during NFs. Compared with NFs, CO 2 emission was significantly decreased by 57% but CH 4 emission was significantly increased by 38 times during Fs in the Carex meadow. Stepwise regression combined with structural equation model analysis showed that CO 2 and CH 4 flux were mainly influenced by Ts during NFs, and they were controlled by water temperature ( Tw ) during Fs. During NFs, CO 2 flux increased with increasing Ts and SWC but decreased significantly when SWC was over 66% and 52% in the Carex meadow and mudflat, respectively. CH 4 flux showed an emission pulse at SWC and Ts of 65% and 17.2 °C, respectively. These results indicate that flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission. The continuous decrease of flooding days caused by anthropogenic disturbances may induce soil C loss in Dongting Lake wetlands. , Key Points Soil C emission in Dongting Lake floodplain was 264.3–307.8 g C.m ‐2 .year ‐1 Flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission The decline of flooding days in Dongting Lake wetlands can potentially increase soil C loss

How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPPEC). Temperature can explain most of the seasonal variation in VIs and GPPEC fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPPVPM) and GPPEC indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPPEC fluxes in terms of patterns and magnitudes, explaining about 85% of GPPEC changes over the study years (p < 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink/source.