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The main objective of the present study was to determine the contributions of autotrophic and heterotrophic components to the total soil CO 2 efflux over three years with high-frequency data acquisition by means of automated measurements. Soil CO 2 efflux was measured continuously by using an automated open system of 10 soil respiration chambers in a sandy grassland in Hungary. Mesh-collar technique was applied to separate the components of the total respiration. Data were collected (1) in root-exclusion (Exr), (2) in root- and mycorrhiza exclusion (Exrm) and (3) in control plots (Exc, roots and mycorrhiza included). We fitted three different models to describe the dependence of total soil CO 2 efflux measured on the Exc (Rs), CO 2 efflux measured on Exr (R TR ) and CO 2 efflux measured on Exrm (R TRM ) on abiotic and biotic drivers. The best fitted model (based on AIC) was later on used in a simulation process. The contribution by rhizospheric respiration (simulated, R rhizo* ) was 36 ± 21%, the contribution by mycorrhizal respiration (simulated, R myc* ) to the total soil respiration was 9 ± 9% while the contribution by heterotrophic respiration (simulated, R het* ) was 55 ± 21% on average. Measured mycorrhizal respiration (R M ) responded to GPP with a time lag of 0–2 days in active period. Drought affected the autotrophic component of soil respiration the most intensively: raise of soil water content resulted in increase of R S by 175% while R TR and R TRM increased by 127% and 93%, respectively. The results highlight the fact that it would be useful to establish and apply separate models for each component. [ABSTRACT FROM AUTHOR]

Abstract: High variability of soil respiration measured at fine spatial scale increases the uncertainty when trying to determine the representative average soil respiration (Rs) flux. A possible way to decrease the uncertainty, while also optimising measurement effort, could be the calculation of required number of Rs measurements (Nopt) together with the optimisation of their spatial arrangement. The goals of this study were to find explanatory variables of the structural parameters of fine-scale Rs spatial pattern and of Nopt for sampling optimisation. We conducted field Rs, soil temperature (Ts) and soil water content (SWC) measurements in a Hungarian sandy pasture over several years along circular transects of 75 evenly spaced (20cm distance) sampling positions. Structural parameters of patterns of Rs and the covariates were determined from variograms. Ranges of spatial autocorrelation varied between 0 and 3.6m for Rs, 0 and 3m for SWC and 0 and 2.9m for Ts. Patch size of Rs depended negatively on transect average SWC. To understand and quantify the spatial dependence of variables, cross-variograms were calculated. Rs proved to be positively spatially correlated to SWC at low water supply, while Ts-Rs and SWC-Ts spatial correlations were mostly negative, both due the direct effect of evaporative cooling on Ts. We found that spatial patchiness became less robust and the correlations generally decreased as soil moisture content was high. We found that explanatory variable of Nopt was also SWC, with negative correlation between them. We conclude that sampling could be optimized on the basis of the easily measurable actual SWC, which determines both the optimal number of Rs measurements and the minimum distances between individual samples in semi-arid ecosystems. [Copyright &y& Elsevier]