Search

As limited resources, soils are the largest terrestrial sinks of organic carbon. In this respect, 3D modelling of soil organic carbon (SOC) offers substantial improvements in the understanding and assessment of the spatial distribution of SOC stocks. Previous three-dimensional SOC modelling approaches usually averaged each depth increment for multi-layer two-dimensional predictions. Therefore, these models are limited in their vertical resolution and thus in the interpretability of the soil as a volume as well as in the accuracy of the SOC stock predictions. So far, only few approaches used spatially modelled depth functions for SOC predictions. This study implemented and evaluated an approach that compared polynomial, logarithmic and exponential depth functions using non-linear machine learning techniques, i.e. multivariate adaptive regression splines, random forests and support vector machines to quantify SOC stocks spatially and depth-related in the context of biodiversity and ecosystem functioning research. The legacy datasets used for modelling include profile data for SOC and bulk density (BD), sampled at five depth increments (0-5, 5-10, 10-20, 20-30, 30-50 cm). The samples were taken in an experimental forest in the Chinese subtropics as part of the biodiversity and ecosystem functioning (BEF) China experiment. Here we compared the depth functions by means of the results of the different machine learning approaches obtained based on multi-layer 2D models as well as 3D models. The main findings were (i) that 3rd degree polynomials provided the best results for SOC and BD (R2 = 0.99 and R2 = 0.98; RMSE = 0.36% and 0.07 g cm-3). However, they did not adequately describe the general asymptotic trend of SOC and BD. In this respect the exponential (SOC: R2 = 0.94; RMSE = 0.56%) and logarithmic (BD: R2 = 84; RMSE = 0.21 g cm-3) functions provided more reliable estimates. (ii) random forests with the exponential function for SOC correlated better with the corresponding 2.5D predictions (R2: 0.96 to 0.75), compared to the 3rd degree polynomials (R2: 0.89 to 0.15) which support vector machines fitted best. We recommend not to use polynomial functions with sparsely sampled profiles, as they have many turning points and tend to overfit the data on a given profile. This may limit the spatial prediction capacities. Instead, less adaptive functions with a higher degree of generalisation such as exponential and logarithmic functions should be used to spatially map sparse vertical soil profile datasets. We conclude that spatial prediction of SOC using exponential depth functions, in conjunction with random forests is well suited for 3D SOC stock modelling, and provides much finer vertical resolutions compared to 2.5D approaches., Competing Interests: The authors have declared that no competing interests exist.

Any release of anthrax spores in the U.S. would require action to decontaminate the site and restore its use and operations as rapidly as possible. The remediation activity would require environmental sampling, both initially to determine the extent of contamination (hazard mapping) and post-decon to determine that the site is free of contamination (clearance sampling). Whether the spore contamination is within a building or outdoors, collecting and analyzing what could be thousands of samples can become the factor that limits the pace of restoring operations. To address this sampling and analysis bottleneck and decrease the time needed to recover from an anthrax contamination event, this study investigates the use of composite sampling. Pooling or compositing of samples is an established technique to reduce the number of analyses required, and its use for anthrax spore sampling has recently been investigated. However, use of composite sampling in an anthrax spore remediation event will require well-documented and accepted methods. In particular, previous composite sampling studies have focused on sampling from hard surfaces; data on soil sampling are required to extend the procedure to outdoor use. Further, we must consider whether combining liquid samples, thus increasing the volume, lowers the sensitivity of detection and produces false negatives. In this study, methods to composite bacterial spore samples from soil are demonstrated. B. subtilis spore suspensions were used as a surrogate for anthrax spores. Two soils (Arizona Test Dust and sterilized potting soil) were contaminated and spore recovery with composites was shown to match individual sample performance. Results show that dilution can be overcome by concentrating bacterial spores using standard filtration methods. This study shows that composite sampling can be a viable method of pooling samples to reduce the number of analysis that must be performed during anthrax spore remediation.

Soil erosion is a key threat to many ecosystems, especially in subtropical China where high erosion rates occur. While the mechanisms that induce soil erosion on agricultural land are well understood, soil erosion processes in forests have rarely been studied. Throughfall kinetic energy (TKE) is influenced in manifold ways and often determined by the tree's leaf and architectural traits. We investigated the role of species identity in mono-specific stands on TKE by asking to what extent TKE is species-specific and which leaf and architectural traits account for variation in TKE. We measured TKE of 11 different tree species planted in monocultures in a biodiversity-ecosystem-functioning experiment in subtropical China, using sand-filled splash cups during five natural rainfall events in summer 2013. In addition, 14 leaf and tree architectural traits were measured and linked to TKE. Our results showed that TKE was highly species-specific. Highest TKE was found below Choerospondias axillaris and Sapindus saponaria, while Schima superba showed lowest TKE. These species-specific effects were mediated by leaf habit, leaf area (LA), leaf pinnation, leaf margin, stem diameter at ground level (GD), crown base height (CBH), tree height, number of branches and leaf area index (LAI) as biotic factors and throughfall as abiotic factor. Among these, leaf habit, tree height and LA showed the highest effect sizes on TKE and can be considered as major drivers of TKE. TKE was positively influenced by LA, GD, CBH, tree height, LAI, and throughfall amount while it was negatively influenced by the number of branches. TKE was lower in evergreen, simple leaved and dentate leaved than in deciduous, pinnated or entire leaved species. Our results clearly showed that soil erosion in forest plantations can be mitigated by the appropriate choice of tree species.

As CO2 concentrations continue to rise and drive global climate change, much effort has been put into estimating soil carbon (C) stocks and dynamics over time. However, the inconsistent methods employed by researchers hamper the comparability of such works, creating a pressing need to standardize the methods for soil organic C (SOC) quantification by the various methods. Here, we collected 712 soil samples from 36 sites of alpine grasslands on the Tibetan Plateau covering different soil depths and vegetation and soil types. We used an elemental analyzer for soil total C (STC) and an inorganic carbon analyzer for soil inorganic C (SIC), and then defined the difference between STC and SIC as SOCCNS. In addition, we employed the modified Walkley-Black (MWB) method, hereafter SOCMWB. Our results showed that there was a strong correlation between SOCCNS and SOCMWB across the data set, given the application of a correction factor of 1.103. Soil depth and soil type significantly influenced on the recovery, defined as the ratio of SOCMWB to SOCCNS, and the recovery was closely associated with soil carbonate content and pH value as well. The differences of recovery between alpine meadow and steppe were largely driven by soil pH. In addition, statistically, a relatively strong correlation between SOCCNS and STC was also found, suggesting that it is feasible to estimate SOCCNS stocks through the STC data across the Tibetan grasslands. Therefore, our results suggest that in order to accurately estimate the absolute SOC stocks and its change in the Tibetan alpine grasslands, adequate correction of the modified WB measurements is essential with correct consideration of the effects of soil types, vegetation, soil pH and soil depth.

Fungal diversity and community composition are mainly related to soil and vegetation factors. However, the relative contribution of the different drivers remains largely unexplored, especially in subtropical forest ecosystems. We studied the fungal diversity and community composition of soils sampled from 12 comparative study plots representing three forest age classes (Young: 10-40 yrs; Medium: 40-80 yrs; Old: ≥80 yrs) in Gutianshan National Nature Reserve in South-eastern China. Soil fungal communities were assessed employing ITS rDNA pyrotag sequencing. Members of Basidiomycota and Ascomycota dominated the fungal community, with 22 putative ectomycorrhizal fungal families, where Russulaceae and Thelephoraceae were the most abundant taxa. Analysis of similarity showed that the fungal community composition significantly differed among the three forest age classes. Forest age class, elevation of the study plots, and soil organic carbon (SOC) were the most important factors shaping the fungal community composition. We found a significant correlation between plant and fungal communities at different taxonomic and functional group levels, including a strong relationship between ectomycorrhizal fungal and non-ectomycorrhizal plant communities. Our results suggest that in subtropical forests, plant species community composition is the main driver of the soil fungal diversity and community composition.

The Tibetan Plateau reacts particularly sensitively to possible effects of climate change. Approximately two thirds of the total area is affected by permafrost. To get a better understanding of the role of permafrost on soil organic carbon pools and stocks, investigations were carried out including both discontinuous (site Huashixia, HUA) and continuous permafrost (site Wudaoliang, WUD). Three organic carbon fractions were isolated using density separation combined with ultrasonic dispersion: the light fractions (<1.6 g cm(-3)) of free particulate organic matter (FPOM) and occluded particulate organic matter (OPOM), plus a heavy fraction (>1.6 g cm(-3)) of mineral associated organic matter (MOM). The fractions were analyzed for C, N, and their portion of organic C. FPOM contained an average SOC content of 252 g kg(-1). Higher SOC contents (320 g kg(-1)) were found in OPOM while MOM had the lowest SOC contents (29 g kg(-1)). Due to their lower density the easily decomposable fractions FPOM and OPOM contribute 27% (HUA) and 22% (WUD) to the total SOC stocks. In HUA mean SOC stocks (0-30 cm depth) account for 10.4 kg m(-2), compared to 3.4 kg m(-2) in WUD. 53% of the SOC is stored in the upper 10 cm in WUD, in HUA only 39%. Highest POM values of 36% occurred in profiles with high soil moisture content. SOC stocks, soil moisture and active layer thickness correlated strongly in discontinuous permafrost while no correlation between SOC stocks and active layer thickness and only a weak relation between soil moisture and SOC stocks could be found in continuous permafrost. Consequently, permafrost-affected soils in discontinuous permafrost environments are susceptible to soil moisture changes due to alterations in quantity and seasonal distribution of precipitation, increasing temperature and therefore evaporation.

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO(2) m(-2) s(-1), ranging from 0.39 to 12.88 µmol CO(2) m(-2) s(-1), with average daily mean Rs of 2.01 and 5.49 µmol CO(2) m(-2) s(-1) for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale.