Search

Accurate estimation of regional and global patterns of ecosystem respiration (ER) is crucial to improve the understanding of terrestrial carbon cycles and the predictive ability of the global carbon budget. However, large uncertainties still exist in regional and global ER estimation due to the drawbacks of modeling methods. Based on eddy covariance ER data from 132 sites in China from 2002 to 2020, we established Intelligent Random Forest (IRF) models that integrated ecological understanding with machine learning techniques to estimate ER. The results showed that the IRF models performed better than semiempirical models and machine learning algorithms. The observed data revealed that gross primary productivity (GPP), living plant biomass, and soil organic carbon (SOC) were of great importance in controlling the spatiotemporal variability of ER across China. An optimal model governed by annual GPP, living plant biomass, SOC, and air temperature (IRF‐04 model) matched 93% of the spatiotemporal variation in site‐level ER, and was adopted to evaluate the spatiotemporal pattern of ER in China. Using the optimal model, we obtained that the annual value of ER in China ranged from 5.05 to 5.84 Pg C yr−1 between 2000 and 2020, with an average value of 5.53 ± 0.22 Pg C yr−1. In this study, we suggest that future models should integrate process‐based and data‐driven approaches for understanding and evaluating regional and global carbon budgets. Plain Language Summary: With China already committing to achieve carbon neutrality before 2060, an accurate assessment of land carbon sink and its flux rate in China is an increasingly important area in global change ecology. In this essay, a high‐efficiency and accurate simulation method was introduced in this field; This method is particularly useful in the assessment of carbon sink and its flux rate in China by combining with reliable observation flux data. Using this new method, a reliable and reasonable value of carbon flux (ecosystem respiration) was obtained. Meanwhile, that method provides a better understanding of the mechanism governing the spatiotemporal variability of carbon flux. Therefore, this present study has gone some way toward enhancing our understanding of a comprehensive assessment and analysis of land carbon sink in China. Key Points: A model integrating ecological knowledge and machine learning was established to estimate ecosystem respiration (ER) in ChinaThe spatiotemporal patterns of ER are significantly affected by productivity, plant biomass, soil organic carbon, and air temperatureChina's ER was estimated to be 5.53 ± 0.22 petagrams of carbon per year (Pg C yr−1) on average for the years 2000–2020 [ABSTRACT FROM AUTHOR]

Ecosystem light use efficiency (LUE) is a critical parameter in estimating CO2 uptake by vegetation from climatological and satellite data. However, the spatiotemporal dynamics and biophysical regulations of ecosystem‐level LUE are not well understood, resulting in large uncertainties in the estimation of gross primary productivity (GPP) using LUE‐based models. In this study, we used eddy covariance to explore spatiotemporal variations and controls of LUE in four contrasting forest ecosystems (savanna, tropical rainforest, subtropical evergreen forest, and subalpine coniferous forest). Based on 27 site years of data, we found that (1) the multiyear mean LUE was 0.063, 0.251, 0.247, and 0.140 g C· mol photon−1 in the four contrasting ecosystems, respectively; (2) the LUE in the wet season (May–October) was higher than that in the dry season in all studied ecosystems; (3) the leaf area index controlled GPP and LUE significantly and explained 74%, 29%, 54%, and 36% of the variation in GPP and 51%, 19%, 41%, and 54% of the variation in LUE in the four contrasting ecosystems, respectively; (4) path analysis revealed the critical roles of GPP and vapor pressure deficit in controlling LUE in these four forest ecosystems; and (5) under warming scenarios, LUE may decrease in savanna but increase in the other three ecosystems, while decreasing precipitation (P) may reduce LUE in the ecosystems studied. This study improves our understanding of the influence of biophysical factors on LUE and demonstrates how LUE changes with variations in temperature, soil moisture, and leaf area index, thereby improving estimations of large‐scale carbon exchange/cycling. Key Points: Evergreen broad‐leaved forest (EBF) ecosystem shows higher LUE than subalpine coniferous/savanna ecosystemsThe vapor pressure deficit (VPD) and leaf area index (LAI) largely determine the variability in LUEWarming might decrease LUE in the savanna ecosystem and may increase LUE in the other three ecosystems [ABSTRACT FROM AUTHOR]

In order to predict the effects of climate change on the global carbon cycle, it is crucial to understand the environmental factors that affect soil carbon storage in grasslands. In the present study, we attempted to explain the relationships between the distribution of soil carbon storage with climate, soil types, soil properties and topographical factors across different types of grasslands with different grazing regimes. We measured soil organic carbon in 92 locations at different soil depth increments, from 0 to 100 cm in southwestern China. Among soil types, brown earth soils (Luvisols) had the highest carbon storage with 19.5 ± 2.5 kg m−2, while chernozem soils had the lowest with 6.8 ± 1.2 kg m−2. Mean annual temperature and precipitation, exerted a significant, but, contrasting effects on soil carbon storage. Soil carbon storage increased as mean annual temperature decreased and as mean annual precipitation increased. Across different grassland types, the mean carbon storage for the top 100 cm varied from 7.6 ± 1.3 kg m−2 for temperate desert to 17.3 ± 2.9 kg m−2 for alpine meadow. Grazing/cutting regimes significantly affected soil carbon storage with lowest value (7.9 ± 1.5 kg m−2) recorded for cutting grass, while seasonal (11.4 ± 1.3 kg m−2) and year-long (12.2 ± 1.9 kg m−2) grazing increased carbon storage. The highest carbon storage was found in the completely ungrazed areas (16.7 ± 2.9 kg m−2). Climatic factors, along with soil types and topographical factors, controlled soil carbon density along a soil depth in grasslands. Environmental factors alone explained about 60% of the total variation in soil carbon storage. The actual depth-wise distribution of soil carbon contents was significantly influenced by the grazing intensity and topographical factors. Overall, policy-makers should focus on reducing the grazing intensity and land conversion for the sustainable management of grasslands and C sequestration. • Soil carbon storage was compared among grasslands of Yunnan province, China. • Mean soil carbon density was 12.1 kg C m−2 in the top 100 cm soil layer. • Seasonal and year-long grazed grasslands also had greater soil carbon storage. • MAT, MAP, soil type and topography significantly affected soil carbon storage. • Environmental factors explained 60% of the total variation in soil carbon density. [ABSTRACT FROM AUTHOR]

Background: Gut microbiota dysbiosis is reportedly actively involved in autoimmune diseases such as type 1 diabetes mellitus (T1DM). However, the alterations in the gut microbiota and their correlation with fasting blood glucose (FBG) in Chinese children with T1DM remain unclear., Aim: To investigate alterations in the gut microbiota in Chinese children with T1DM and their associations with clinical indicators., Methods: Samples from 51 children with T1DM and 47 age-matched and gender-matched healthy controls were obtained, to explore the structural and functional alterations in the fecal microbiota. The V3-V4 regions of the 16S rRNA gene were sequenced on a MiSeq instrument, and the association with FBG were analyzed., Results: We found that the bacterial diversity was significantly increased in the T1DM-associated fecal microbiota, and changes in the microbial composition were observed at different taxonomic levels. The T1DM-reduced differential taxa, such as Bacteroides vulgatus ATCC8482, Bacteroides ovatus , Bacteroides xylanisolvens , and Flavonifractor plautii , were negatively correlated with FBG, while the T1DM-enriched taxa, such as Blautia , Eubacterium hallii group, Anaerostipes hadrus , and Dorea longicatena , were positively correlated with FBG. Bacteroides vulgatus ATCC8482, Bacteroides ovatus , the Eubacterium hallii group, and Anaerostipes hadrus , either alone or in combination, could be used as noninvasive diagnostic biomarkers to discriminate children with T1DM from healthy controls. In addition, the functional changes in the T1DM-associated fecal microbiota also suggest that these fecal microbes were associated with altered functions and metabolic activities, such as glycan biosynthesis and metabolism and lipid metabolism, which might play vital roles in the pathogenesis and development of T1DM., Conclusion: Our present comprehensive investigation of the T1DM-associated fecal microbiota provides novel insights into the pathogenesis of the disease and sheds light on the diagnosis and treatment of T1DM., Competing Interests: Conflict-of-interest statement: There are no conflicts of interest to report., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)

By using eddy covariance technique, this paper quantitatively analyzed the photosynthetic characteristics of tropical seasonal rainforest ecosystem and related environmental controlling factors in Xishuangbanna in 2003-2006. In the study period, less interannual difference was observed in the net photosynthesis of the ecosystem, with the maximum photosynthesis rate (P(eco,opt)), respiration at daytime (R(eco,d)), and apparent quantum yield (alpha) averaged by 0.813 mg x m(-2) x s(-1), 0.238 mg x m(-2) x s(-1), and 0.0023 mg x micromol(-1), respectively. As affected by the interaction of air temperature (Ta) and vapor pressure deficit (VPD), the photosynthetic characteristics had some seasonal differences. In rainy season, the ecosystem had the strongest photosynthetic capacity because of the higher precipitation and warmer air temperature; in foggy and cool season, fog drip played an important role in the water relations of plants, and thereby, the ecosystem photosynthetic capacity was still higher; in dry and hot season, due to the limited precipitation and high temperature, the Ta and VPD increased, inducing a decrease of ecosystem alpha and P(eco,opt). The net CO2 exchange of the ecosystem strongly depended on the Ta above 20 degrees C and the VPD above 1 kPa.

By the methods of chamber-based and eddy covariance measurements, the CO2 exchange of dominant tree species Pometia tomentosa and Gironniera subaequalis at leaf and canopy levels in the tropical seasonal rain forest of Xishuangbanna was measured in different seasons of 2004. The results showed that for the two tree species, the maximum net photosynthesis (P(max A)) of canopy based on chamber-based measurement ranked in the order of rainy season (RS) > end of rainy season (ERS) > foggy-cool season (FS) > dry-hot season (DS), and the dark respiration rate (Rd) of leaf was RS > ERS > DS > FS. The P(max B) based on eddy covariance measurement was in the same order as that based on chamber-based measurement, while the canopy respiration rate (Re) was RS > DS > FS > ERS. The maximum net photosynthetic rate of canopy in different seasons measured by the two methods had a comparatively small difference, ranging from 0.9 to 2.0 micromol m(-2) s(-1).