Response of soil respiration to soil temperature and moisture in a 50-year-old oriental arborvitae plantation in China

Soil Depth Significantly Shifted Microbial Community Structures and Functions in a Semiarid Prairie Agroecosystem

The North American Great Plains cover a large area of the Nearctic ecozone, and an important part of this biome is semiarid. The sustainable intensification of agriculture that is necessary to produce food for an ever-increasing world population requires knowledge of the taxonomic and functional structure of the soil microbial community. In this study, we investigated the influence of soil depth on the composition and functions of the microbial communities hosted in agricultural soils of a semiarid agroecosystem, using metagenomic profiling, and compared them to changes in soil chemical and physical properties. Shotgun sequencing was used to determine the composition and functions of the soil microbial community of 45 soil samples from three soil depths (0-15 cm, 15-30 cm, and 30-60 cm) under different agricultural land use types (native prairie, seeded prairie, and cropland) in southwest Saskatchewan. Analysis of community composition revealed the declining abundance of phyla Verrucomicrobia, Bacteroidetes, Chlorophyta, Bacillariophyta, and Acidobacteria with soil depth, whereas the abundance of phyla Ascomycota, Nitrospirae, Planctomycetes, and Cyanobacteria increased with soil depth. Soil functional genes related to nucleosides and nucleotides, phosphorus (P) metabolism, cell division and cell cycle, amino acids and derivatives, membrane transport, and fatty acids were particularly abundant at 30-60 cm. In contrast, functional genes related to DNA and RNA metabolism, metabolism of nitrogen, sulfur and carbohydrates, and stress response were more abundant in the top soil depth. The RDA analysis of functional genes and soil physico-chemical properties revealed a positive correlation between phages and soil organic P concentrations. In the rooting zone of this semiarid agroecosystem, soil microbes express variable structural patterns of taxonomic and functional diversity at different soil depths. This study shows that the soil microbial community is structured by soil depth and physicochemical properties, with the middle soil depth being an intermediate transition zone with a higher taxonomic diversity. Our results suggest the co-existence of various microbial phyla adapted to upper and lower soil depths in an intermediate-depth transition zone.

Model Selection for Ecosystem Respiration Needs to Be Site Specific: Lessons from Grasslands on the Mongolian Plateau

Selecting an appropriate model for simulating ecosystem respiration is critical in modeling the carbon cycle of terrestrial ecosystems due to their magnitude and high variations in time and space. There is no consensus on the ideal model for estimating ecosystem respiration in different ecosystems. We evaluated the performances of six respiration models, including Arrhenius, logistic, Gamma, Martin, Concilio, and time series model, against measured ecosystem respiration during 2014–2018 in four grassland ecosystems on the Mongolian Plateau: shrubland, dry steppe, temperate steppe, and meadow ecosystems. Ecosystem respiration increased exponentially with soil temperature within an apparent threshold of ~19.62 °C at shrubland, ~16.05 °C at dry steppe, ~16.92 °C at temperate steppe, and ~15.03 °C at meadow. The six models explained approximately 50–80% of the variabilities of ecosystem respiration during the study period. Both soil temperature and soil moisture played considerable roles in simulating ecosystem respiration with R square, ranging from 0.5 to 0.8. The Martin model performed better than the other models, with a relatively high R square, i.e., R2 = 0.68 at shrubland, R2 = 0.57 at dry steppe, R2 = 0.74 at temperate steppe, and R2 = 0.81 at meadow. These models achieved good performance for around 50–80% of the simulations. No single model performs best for all four grassland types, while each model appears suitable for at least one type of ecosystem. Models that oil moisture include models, especially the Martin model, are more suitable for the accurate prediction of ecosystem respiration than Ts-only models for the four grassland ecosystems.

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation.

Changes in the Size of the Active Microbial Pool Explain Short-Term Soil Respiratory Responses to Temperature and Moisture

Heterotrophic respiration contributes a substantial fraction of the carbon flux from soil to atmosphere, and responds strongly to environmental conditions. However, the mechanisms through which short-term changes in environmental conditions affect microbial respiration still remain unclear. Microorganisms cope with adverse environmental conditions by transitioning into and out of dormancy, a state in which they minimize rates of metabolism and respiration. These transitions are poorly characterized in soil and are generally omitted from decomposition models. Most current approaches to model microbial control over soil CO2 production relate responses to total microbial biomass (TMB) and do not differentiate between microorganisms in active and dormant physiological states. Indeed, few data for active microbial biomass (AMB) exist with which to compare model output. Here, we tested the hypothesis that differences in soil microbial respiration rates across various environmental conditions are more closely related to differences in AMB (e.g., due to activation of dormant microorganisms) than in TMB. We measured basal respiration (SBR) of soil incubated for a week at two temperatures (24 and 33°C) and two moisture levels (10 and 20% soil dry weight [SDW]), and then determined TMB, AMB, microbial specific growth rate, and the lag time before microbial growth (t lag ) using the Substrate-Induced Growth Response (SIGR) method. As expected, SBR was more strongly correlated with AMB than with TMB. This relationship indicated that each g active biomass C contributed ~0.04 g CO2-C h(-1) of SBR. TMB responded very little to short-term changes in temperature and soil moisture and did not explain differences in SBR among the treatments. Maximum specific growth rate did not respond to environmental conditions, suggesting that the dominant microbial populations remained similar. However, warmer temperatures and increased soil moisture both reduced t lag , indicating that favorable abiotic conditions activated soil microorganisms. We conclude that soil respiratory responses to short-term changes in environmental conditions are better explained by changes in AMB than in TMB. These results suggest that decomposition models that explicitly represent microbial carbon pools should take into account the active microbial pool, and researchers should be cautious in comparing modeled microbial pool sizes with measurements of TMB.

Effects of manipulated above- and belowground organic matter input on soil respiration in a Chinese pine plantation

Alteration in the amount of soil organic matter input can have profound effect on carbon dynamics in forest soils. The objective of our research was to determine the response in soil respiration to above- and belowground organic matter manipulation in a Chinese pine (Pinus tabulaeformis) plantation. Five organic matter treatments were applied during a 2-year experiment: both litter removal and root trenching (LRRT), only litter removal (LR), control (CK), only root trenching (RT) and litter addition (LA). We found that either aboveground litter removal or root trenching decreased soil respiration. On average, soil respiration rate was significantly decreased in the LRRT treatment, by about 38.93% ± 2.01% compared to the control. Soil respiration rate in the LR treatment was 30.65% ± 1.87% and in the RT treatment 17.65% ± 1.95% lower than in the control. Litter addition significantly increased soil respiration rate by about 25.82% ± 2.44% compared to the control. Soil temperature and soil moisture were the main factors affecting seasonal variation in soil respiration. Up to the 59.7% to 82.9% seasonal variation in soil respiration is explained by integrating soil temperature and soil moisture within each of the various organic matter treatments. The temperature sensitivity parameter, Q₁₀, was higher in the RT (2.72) and LA (3.19) treatments relative to the control (2.51), but lower in the LRRT (1.52) and LR treatments (1.36). Our data suggest that manipulation of soil organic matter input can not only alter soil CO₂ efflux, but also have profound effect on the temperature sensitivity of organic carbon decomposition in a temperate pine forest.

Soil respiration under different land uses in Eastern China

Land-use change has a crucial influence on soil respiration, which further affects soil nutrient availability and carbon stock. We monitored soil respiration rates under different land-use types (tea gardens with three production levels, adjacent woodland, and a vegetable field) in Eastern China at weekly intervals over a year using the dynamic closed chamber method. The relationship between soil respiration and environmental factors was also evaluated. The soil respiration rate exhibited a remarkable single peak that was highest in July/August and lowest in January. The annual cumulative respiration flux increased by 25.6% and 20.9% in the tea garden with high production (HP) and the vegetable field (VF), respectively, relative to woodland (WL). However, no significant differences were observed between tea gardens with medium production (MP), low production (LP), WL, and VF. Soil respiration rates were significantly and positively correlated with organic carbon, total nitrogen, and available phosphorous content. Each site displayed a significant exponential relationship between soil respiration and soil temperature measured at 5 cm depth, which explained 84–98% of the variation in soil respiration. The model with a combination of soil temperature and moisture was better at predicting the temporal variation of soil respiration rate than the single temperature model for all sites. Q₁₀ was 2.40, 2.00, and 1.86–1.98 for VF, WL, and tea gardens, respectively, indicating that converting WL to VF increased and converting to tea gardens decreased the sensitivity of soil respiration to temperature. The equation of the multiple linear regression showed that identical factors, including soil organic carbon (SOC), soil water content (SWC), pH, and water soluble aluminum (WSAl), drove the changes in soil respiration and Q₁₀ after conversion of land use. Temporal variations of soil respiration were mainly controlled by soil temperature, whereas spatial variations were influenced by SOC, SWC, pH, and WSAl.

Effects of soil moisture on the temperature sensitivity of soil heterotrophic respiration: a laboratory incubation study

The temperature sensitivity (Q₁₀) of soil heterotrophic respiration (R_h) is an important ecological model parameter and may vary with temperature and moisture. While Q₁₀ generally decreases with increasing temperature, the moisture effects on Q₁₀ have been controversial. To address this, we conducted a 90-day laboratory incubation experiment using a subtropical forest soil with a full factorial combination of five moisture levels (20%, 40%, 60%, 80%, and 100% water holding capacity - WHC) and five temperature levels (10, 17, 24, 31, and 38°C). Under each moisture treatment, R_h was measured several times for each temperature treatment to derive Q₁₀ based on the exponential relationships between R_h and temperature. Microbial biomass carbon (MBC), microbial community structure and soil nutrients were also measured several times to detect their potential contributions to the moisture-induced Q₁₀ variation. We found that Q₁₀ was significantly lower at lower moisture levels (60%, 40% and 20% WHC) than at higher moisture level (80% WHC) during the early stage of the incubation, but became significantly higher at 20%WHC than at 60% WHC and not significantly different from the other three moisture levels during the late stage of incubation. In contrast, soil R_h had the highest value at 60% WHC and the lowest at 20% WHC throughout the whole incubation period. Variations of Q₁₀ were significantly associated with MBC during the early stages of incubation, but with the fungi-to-bacteria ratio during the later stages, suggesting that changes in microbial biomass and community structure are related to the moisture-induced Q₁₀ changes. This study implies that global warming’s impacts on soil CO₂ emission may depend upon soil moisture conditions. With the same temperature rise, wetter soils may emit more CO₂ into the atmosphere via heterotrophic respiration.

Temperature response of soil respiration in a Chinese pine plantation: hysteresis and seasonal vs. diel Q10

Although the temperature response of soil respiration (Rs ) has been studied extensively, several issues remain unresolved, including hysteresis in the Rs -temperature relationship and differences in the long- vs. short-term Rs sensitivity to temperature. Progress on these issues will contribute to reduced uncertainties in carbon cycle modeling. We monitored soil CO2 efflux with an automated chamber system in a Pinus tabulaeformis plantation near Beijing throughout 2011. Soil temperature at 10-cm depth (Ts ) exerted a strong control over Rs , with the annual temperature sensitivity (Q10) and basal rate at 10°C (Rs10) being 2.76 and 1.40 µmol m(-2) s(-1), respectively. Both Rs and short-term (i.e., daily) estimates of Rs10 showed pronounced seasonal hysteresis with respect to Ts , with the efflux in the second half of the year being larger than that early in the season for a given temperature. The hysteresis may be associated with the confounding effects of microbial population dynamics and/or litter input. As a result, all of the applied regression models failed to yield unbiased estimates of Rs over the entire annual cycle. Lags between Rs and Ts were observed at the diel scale in the early and late growing season, but not in summer. The seasonality in these lags may be due to the use of a single Ts measurement depth, which failed to represent seasonal changes in the depth of CO2 production. Daily estimates of Q10 averaged 2.04, smaller than the value obtained from the seasonal relationship. In addition, daily Q10 decreased with increasing Ts , which may contribute feedback to the climate system under global warming scenarios. The use of a fixed, universal Q10 is considered adequate when modeling annual carbon budgets across large spatial extents. In contrast, a seasonally-varying, environmentally-controlled Q10 should be used when short-term accuracy is required.

Carbon dioxide flux from rice paddy soils in central China: effects of intermittent flooding and draining cycles

A field experiment was conducted to (i) examine the diurnal and seasonal soil carbon dioxide (CO₂) fluxes pattern in rice paddy fields in central China and (ii) assess the role of floodwater in controlling the emissions of CO₂ from soil and floodwater in intermittently draining rice paddy soil. The soil CO₂ flux rates ranged from −0.45 to 8.62 µmol.m⁻².s⁻¹ during the rice-growing season. The net effluxes of CO₂ from the paddy soil were lower when the paddy was flooded than when it was drained. The CO₂ emissions for the drained conditions showed distinct diurnal variation with a maximum efflux observed in the afternoon. When the paddy was flooded, daytime soil CO₂ fluxes reversed with a peak negative efflux just after midday. In draining/flooding alternating periods, a sudden pulse-like event of rapidly increasing CO₂ efflux occured in response to re-flooding after draining. Correlation analysis showed a negative relation between soil CO₂ flux and temperature under flooded conditions, but a positive relation was found under drained conditions. The results showed that draining and flooding cycles play a vital role in controlling CO₂ emissions from paddy soils.

Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment

Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. However, few interpolation methods perform satisfactorily for complex terrains. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. A data set of 153 soil water profiles (1 m) from the semiarid hilly gully Loess Plateau of China was used, generated under a wide range of land use types, vegetation types and topographic positions. Four spatial interpolation methods, including ordinary kriging, inverse distance weighting, linear regression and regression kriging were used for modeling, randomly partitioning the data set into 2/3 for model fit and 1/3 for independent testing. The performance of each method was assessed quantitatively in terms of mean-absolute-percentage-error, root-mean-square-error, and goodness-of-prediction statistic. The results showed that the prediction accuracy differed significantly between each method in complex terrain. The ordinary kriging and inverse distance weighted methods performed poorly due to the poor spatial autocorrelation of soil moisture at small catchment scale with complex terrain, where the environmental impact factors were discontinuous in space. The linear regression model was much more suitable to the complex terrain than the former two distance-based methods, but the predicted soil moisture changed too sharply near the boundary of the land use types and junction of the sunny (southern) and shady (northern) slopes, which was inconsistent with reality because soil moisture should change gradually in short distance due to its mobility in soil. The most optimal interpolation method in this study for the complex terrain was the hybrid regression kriging, which produced a detailed, reasonable prediction map with better accuracy and prediction effectiveness.

Temporal-spatial variation and controls of soil respiration in different primary succession stages on glacier forehead in Gongga Mountain, China

Soil respiration (SR) is an important process in the global carbon cycle. It is difficult to estimate SR emission accurately because of its temporal and spatial variability. Primary forest succession on Glacier forehead provides the ideal environment for examining the temporal-spatial variation and controlling factors of SR. However, relevant studies on SR are relatively scarce, and variations, as well as controlling factors, remain uncertain in this kind of region. In this study, we used a static chamber system to measure SR in six sites which represent different stages of forest succession on forehead of a temperate glacier in Gongga Mountain, China. Our results showed that there was substantial temporal (coefficient of variation (CV) ranged from 39.3% to 73.9%) and spatial (CV ranged from 12.3% to 88.6%) variation in SR. Soil temperature (ST) at 5 cm depth was the major controlling factor of temporal variation in all six sites. Spatial variation in SR was mainly caused by differences in plant biomass and Total N among the six sites. Moreover, soil moisture (SM), microbial biomass carbon (MBC), soil organic carbon (SOC), pH and bulk density could influence SR by directly or indirectly affecting plant biomass and Total N. Q₁₀ values (ranged from 2.1 to 4.7) increased along the forest succession, and the mean value (3.3) was larger than that of temperate ecosystems, which indicated a general tendency towards higher-Q₁₀ in colder ecosystems than in warmer ecosystems. Our findings provided valuable information for understanding temporal-spatial variation and controlling factors of SR.