Mechanism underlying temperature sensitivity of soil organic carbon decomposition: A review.

Temperature sensitivity (Q10) of soil organic carbon (SOC) decomposition is an important index to estimate the dynamics of soil C budget. However, the spatial variation of Q10 and its influencing factors remain largely uncertain. In this study, we reviewed the effects of climate environment, spatial geographic pattern, soil physicochemical property, vegetation type, microbial community composition and function, and global climate change on Q10 to summarize the general rule of each factor influencing Q10 and compare the relative contribution of each factor to Q10 in different ecosystems. The results showed that Q10 decreases with the increases of temperature and precipitation, but increases with the rise of latitude and altitude. The Q10 value is higher in grassland than that in forest, and also in coniferous forest and deciduous forest than that in evergreen broad-leaved forest. Carbon quality is negatively correlated with Q10, but the C quality hypothesis is not always valid with exogenous substrate input. For example, the increment of substrate availability may significantly increase Q10 in low-quality soils. Q10 decreases with the enhanced proportion of r-strategy microorganisms (Proteobacteria and Ascomycetes), but increases with the enhanced proportion of K-strategy microorganisms (Acidobacteria and Basidiomycetes). Q10 increases with elevated CO2 concentration, but declines with atmospheric nitrogen deposition. In natural ecosystems, Q10 is mainly regulated by temperature and C quality. Temperature is the main factor regulating Q10 in the topsoil while C quality is the main factor in deep soil. Our review provided a theoretical support to improve the coupled climate-C cycle model and achieved the C neutral strategy under global warming.

[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].

With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.

Antecedents and outcomes of abusive supervision: test of a trickle-down model.

The authors examined antecedents of abusive supervision and the relative importance of interactional and procedural justice as mediators of the relationship between abusive supervision and the work outcomes of affective organizational commitment and individual- and organization-directed citizenship behaviors. Data were obtained from subordinate-supervisor dyads from a telecommunication company located in southeastern China. Results of moderated regression analysis revealed that authoritarian leadership style moderated the relationship between supervisors' perceptions of interactional justice and abusive supervision such that the relationship was stronger for supervisors high rather than low in authoritarian leadership style. In addition, results of structural equation modeling analysis revealed that subordinates' perceptions of interactional but not procedural justice fully mediated the relationship between abusive supervision and the work outcomes. Implications for future investigations of abusive supervision are discussed.