Effect of moisture on leaf litter decomposition and its contribution to soil respiration in a temperate forest

The degree to which increased soil respiration rates following wetting is caused by plant (autotrophic) versus microbial (heterotrophic) processes, is still largely uninvestigated. Incubation studies suggest microbial processes play a role but it remains unclear whether there is a stimulation of the microbial population as a whole or an increase in the importance of specific substrates that become available with wetting of the soil. We took advantage of an ongoing manipulation of leaf litter <sup>14</sup>C contents at the Oak Ridge Reservation, Oak Ridge, Tennessee, to (1) determine the degree to which an increase in soil respiration rates that accompanied wetting of litter and soil, following a short period of drought, could be explained by heterotrophic contributions; and (2) investigate the potential causes of increased heterotrophic respiration in incubated litter and 0–5 cm mineral soil. The contribution of leaf litter decomposition increased from 6 ± 3 mg C m<sup>−2</sup> hr<sup>−1</sup> during a transient drought, to 63 ± 18 mg C m<sup>−2</sup> hr<sup>−1</sup> immediately after water addition, corresponding to an increase in the contribution to soil respiration from 5 ± 2% to 37 ± 8%. The increased relative contribution was sufficient to explain all of the observed increase in soil respiration for this one wetting event in the late growing season. Temperature (13°C versus 25°C) and moisture (dry versus field capacity) conditions did not change the relative contributions of different decomposition substrates in incubations, suggesting that more slowly cycling C has at least the same sensitivity to decomposition as faster cycling organic C at the temperature and moisture conditions studied

The Childhood Acute Illness and Nutrition (CHAIN) network nested case-cohort study protocol: a multi-omics approach to understanding mortality among children in sub-Saharan Africa and South Asia

Introduction: Many acutely ill children in low- and middle-income settings have a high risk of mortality both during and after hospitalisation despite guideline-based care. Understanding the biological mechanisms underpinning mortality may suggest optimal pathways to target for interventions to further reduce mortality. The Childhood Acute Illness and Nutrition (CHAIN) Network ( www.chainnnetwork.org) Nested Case-Cohort Study (CNCC) aims to investigate biological mechanisms leading to inpatient and post-discharge mortality through an integrated multi-omic approach. Methods and analysis; The CNCC comprises a subset of participants from the CHAIN cohort (1278/3101 hospitalised participants, including 350 children who died and 658 survivors, and 270/1140 well community children of similar age and household location) from nine sites in six countries across sub-Saharan Africa and South Asia. Systemic proteome, metabolome, lipidome, lipopolysaccharides, haemoglobin variants, toxins, pathogens, intestinal microbiome and biomarkers of enteropathy will be determined. Computational systems biology analysis will include machine learning and multivariate predictive modelling with stacked generalization approaches accounting for the different characteristics of each biological modality. This systems approach is anticipated to yield mechanistic insights, show interactions and behaviours of the components of biological entities, and help develop interventions to reduce mortality among acutely ill children. Ethics and dissemination. The CHAIN Network cohort and CNCC was approved by institutional review boards of all partner sites. Results will be published in open access, peer reviewed scientific journals and presented to academic and policy stakeholders. Data will be made publicly available, including uploading to recognised omics databases. Trial registration NCT03208725

Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

Background A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding Bill & Melinda Gates Foundation OPP1131320

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

This study was supported by the NSF China Programs (Grant No. 31300539 and 31570629) and the Public Welfare Technology Application Research Program of Zhejiang province (Grant No. 2015C31004).Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.Yeshttp://www.plosone.org/static/editorial#pee

Changes in eucalypt litter quality during the first three months of field decomposition in a Congolese plantation

In fast-growing tree plantations, decomposition of leaf litter is considered as a key process of soil fertility. A three-month field experiment, spanning both rainy and dry seasons, was conducted to determine how changes in litter decomposition affect the main parameters of litter quality-namely, the concentrations of phenolic and non-phenolic carbon (C) compounds, nitrogen (N), and fibres, and the litter C mineralization rate. This Study was conducted to test (1) if these changes vary according to the Compound and to the season, and if they are greater for soluble compounds, and (2) if after a three-month period of field decomposition, the chemical composition of the remaining litter drives C mineralization, as measured in laboratory conditions, through a greater influence on the concentration of N and lignin. We found that the concentrations of water- and methanol-soluble phenolic compounds and the concentrations of non-phenolic compounds decreased during decomposition in all plots and in each season, while the fibre and N concentrations increased. The relationships among litter decomposition, C mineralization, and litter quality depended on the season, which strongly suggests that different processes are involved in dry and rainy seasons. The C mineralization rates were driven by soluble organic Compounds in the initial litter and by soluble phenolic compounds in the decomposed litter

Estimation of autotrophic and heterotrophic components of soil respiration by trenching is sensitive to corrections for root decomposition and changes in soil water content

This study aims to assess the effects of corrections for disturbances such as an increased amount of dead roots and an increase in volumetric soil water content on the calculation of soil CO2 efflux partitioning. Soil CO2 efflux, soil temperature and superficial soil water content were monitored in two young beech sites (H1 and H2) during a trenching experiment. Trenching induced a significant input of dead root mass that participated in soil CO2 efflux and reduced the soil dissolved organic carbon content, while it increased superficial soil water content within the trenched plot. Annual soil CO2 efflux in control plots was 528 g C m -2 year-1 at H1 and 527 g C m-2 year -1 at H2. The annual soil CO2 efflux in trenched plots was 353 g C m-2 year-1 at H1 and 425 g C m-2 year-1 at H2. By taking into account annual CO2 efflux from decaying trenched roots, the autotrophic contribution to total soil CO 2 efflux reached 69% at H1 and 54% at H2. The partitioning calculation was highly sensitive to the initial root mass estimated within the trenched plots. Uncertainties in the remaining root mass, the fraction of root C that is incorporated into soil organic matter during root decomposition, and the root decomposition rate constant had a limited impact on the partitioning calculation. Corrections for differences in superficial soil water content had a significant impact on annual respired CO2 despite a limited effect on partitioning. © 2007 Springer Science+Business Media B.V