Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

An integrated pan-tropical biomass map using multiple reference datasets

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

Presentation of meta-analysis plots

Meta-analysis (forest) plots are widely used to show the results from multiple individual randomized trials or observational studies that address the same question, including the assessment of screening markers. They show the between study spread of results and provide a summary estimate of the results from all the studies combined. We here illustrate the advantage of ordering study results by the magnitude of the effect and including a vertical shaded band encompassing the summary 95% confidence interval of the summary estimate to emphasize the uncertainty of the estimate in a way that is more prominent than only displaying a "diamond" around its value.

The effects of anaesthetics on postoperative physiological reactions: a meta-analysis

This study aimed to systematically investigate the effects of different anaesthetics on postoperative physiological reactions compared with placebo. The literature search was conducted using three databases: PubMed, EMBASE, and the Cochrane Library. Studies published from January 1990 to January 2015 were screened. The language was restricted to English. Heterogeneity was analyzed by the Q test and I(2) statistic. A fixed-effect model was used for homogenous data and a random-effects model for heterogeneous data. The odds ratio (OR) and 95% confidence interval (CI) were calculated to monitor the incidences of overall adverse events, arterial blood pressure, and cardiac abnormalities. Sensitivity analysis was performed to estimate the strength of the meta-analysis, and publication bias was analyzed using Egger's test. A total of 24 articles were included in this meta-analysis. There were 1,810 and 1,806 cases in the anaesthetic group and the placebo group, respectively. The incidence of overall adverse events was significantly lower in the anaesthetic group compared with the placebo group (OR = 0.57; 95% CI, 0.38-0.84). No publication bias was observed, and no inverse estimates were calculated using sensitivity analysis. There was no significant difference for the incidence of arterial blood pressure (OR = 4.62; 95% CI, 0.90-23.70) and cardiac abnormalities (OR = 1.18; 95% CI, 0.53-2.63) between the two groups. Although the incidence of overall adverse events was decreased in the anaesthetic group, it is impossible to determine whether the use of anaesthetics during surgical operation has a protective effect on postoperative physiological reactions.