Vegetation and Topographic Control on Spatial Variability of Soil Organic Carbon

Soil organic carbon (SOC) is one of the most important parameters affecting the hydraulic characteristics of natural soils. Despite being rather easy to measure, SOC is known to be highly variable in space. In this study, vegetation, climate, and morphology factors were used to reproduce the spatial distribution of SOC in the mineral horizons of forest and grassland areas in north-western Italy and the feasibility of the approach was evaluated. When the overall sample (114 samples) was analyzed, average annual rainfall and elevation were significant descriptors of the SOC variability. However, a large part of the variability remains unexplained. Two stratification criteria were then adopted, based on vegetation and topographic properties. We obtained an improvement of the quality of the estimates, particularly for grasslands and forests in the absence of local curvatures. These results indicate that the spatial variability of soil organic matter is scarcely reproducible at the regional scale, unless an a-priori reduction of the heterogeneity is applied. A discussion on the feasibility of applying stratification criteria to deal with heterogeneous samples closes the pape

A tale of two lineages: how the strains of the earliest divergent symbiotic Frankia clade spread over the world

It is currently assumed that around 100 million years ago, the common ancestor to the Fabales, Fagales, Rosales and Cucurbitales in Gondwana, developed a root nodule symbiosis with a nitrogen-fixing bacterium. The symbiotic trait evolved first in Frankia cluster-2; thus, strains belonging to this cluster are the best extant representatives of this original symbiont. Most cluster-2 strains could not be cultured to date, except for Frankia coriariae, and therefore many aspects of the symbiosis are still elusive. Based on phylogenetics of cluster-2 metagenome-assembled genomes (MAGs), it has been shown that the genomes of strains originating in Eurasia are highly conserved. These MAGs are more closely related to Frankia cluster-2 in North America than to the single genome available thus far from the southern hemisphere, i.e., from Papua New Guinea. To unravel more biodiversity within Frankia cluster-2 and predict routes of dispersal from Gondwana, we sequenced and analysed the MAGs of Frankia cluster-2 from Coriaria japonica and Coriaria intermedia growing in Japan, Taiwan and the Philippines. Phylogenetic analyses indicate there is a clear split within Frankia cluster-2, separating a continental from an island lineage. Presumably, these lineages already diverged in Gondwana. Based on fossil data on the host plants, we propose that these two lineages dispersed via at least two routes. While the continental lineage reached Eurasia together with their host plants via the Indian subcontinent, the island lineage spread towards Japan with an unknown host plant

Fuzzy clustering with spatial-temporal information

Clustering geographical units based on a set of quantitative features observed at several time occasions requires to deal with the complexity of both space and time information. In particular, one should consider (1) the spatial nature of the units to be clustered, (2) the characteristics of the space of multivariate time trajectories, and (3) the uncertainty related to the assignment of a geographical unit to a given cluster on the basis of the above com- plex features. This paper discusses a novel spatially constrained multivariate time series clustering for units characterised by different levels of spatial proximity. In particular, the Fuzzy Partitioning Around Medoids algorithm with Dynamic Time Warping dissimilarity measure and spatial penalization terms is applied to classify multivariate Spatial-Temporal series. The clustering method has been theoretically presented and discussed using both simulated and real data, highlighting its main features. In particular, the capability of embedding different levels of proximity among units, and the ability of considering time series with different length

Characterization of Archaeal Community in Contaminated and Uncontaminated Surface Stream Sediments

Archaeal communities from mercury and uranium-contaminated freshwater stream sediments were characterized and compared to archaeal communities present in an uncontaminated stream located in the vicinity of Oak Ridge, TN, USA. The distribution of the Archaea was determined by pyrosequencing analysis of the V4 region of 16S rRNA amplified from 12 streambed surface sediments. Crenarchaeota comprised 76% of the 1,670 archaeal sequences and the remaining 24% were from Euryarchaeota. Phylogenetic analysis further classified the Crenarchaeota as a Freshwater Group, Miscellaneous Crenarchaeota group, Group I3, Rice Cluster VI and IV, Marine Group I and Marine Benthic Group B; and the Euryarchaeota into Methanomicrobiales, Methanosarcinales, Methanobacteriales, Rice Cluster III, Marine Benthic Group D, Deep Sea Hydrothermal Vent Euryarchaeota 1 and Eury 5. All groups were previously described. Both hydrogen- and acetate-dependent methanogens were found in all samples. Most of the groups (with 60% of the sequences) described in this study were not similar to any cultivated isolates, making it difficult to discern their function in the freshwater microbial community. A significant decrease in the number of sequences, as well as in the diversity of archaeal communities was found in the contaminated sites. The Marine Group I, including the ammonia oxidizer Nitrosopumilus maritimus, was the dominant group in both mercury and uranium/nitrate-contaminated sites. The uranium-contaminated site also contained a high concentration of nitrate, thus Marine Group I may play a role in nitrogen cycle

Airborne radiometric survey data and a DTM as covariates for regional scale mapping of soil organic carbon across Northern Ireland

Soil scientists require cost-effective methods to make accurate regional predictions of soil organic carbon (SOC) content. We assess the suitability of airborne radiometric data and digital elevation data as covariates to improve the precision of predictions of SOC from an intensive survey in Northern Ireland. Radiometric data (K band) and, to a lesser extent, altitude are shown to increase the precision of SOC predictions when they are included in linear mixed models of SOC variation. However the statistical distribution of SOC in Northern Ireland is bimodal and therefore unsuitable for geostatistical analysis unless the two peaks can be accounted for by the fixed effects in the linear mixed models. The upper peak in the distribution is due to areas of peat soils. This problem may be partly countered if soil maps are used to classify areas of Northern Ireland according to their expected SOC content and then different models are fitted to each of these classes. Here we divide the soil in Northern Ireland into three classes, namely mineral, organo-mineral and peat. This leads to a further increase in the precision of SOC predictions and the median square error is 2.2 %2. However a substantial number of our observations appear to be mis-classified and therefore the mean squared error in the predictions is larger (30.6 %2) since it is dominated by large errors due to mis-classification. Further improvement in SOC prediction may therefore be possible if better delineation between areas of large SOC (peat) and small SOC (non-peat) could be achieved

Processing of spatial information for mapping of soil organic carbon

Precise and accurate estimates of soil carbon stock (CS) at various scales are key to understanding the potential for terrestrial sequestration of atmospheric CO2. Soil CS exhibits significant field-scale variability due to spatially-varying topography and parent material or past differences in vegetation and management history that affect soil carbon cycling. This limits the accuracy of classical sampling and estimation. We hypothesized that correlated secondary information can aid in the spatial sampling and mapping of soil CS in cases where no prior information on the spatial variation of CS is available. The objectives of this study were to: (a) evaluate different geostatistical methods for incorporating secondary information into the spatial estimation of field-scale CS; (b) identify suitable secondary information for increasing the precision of CS maps; (c) formulate a strategy for utilizing prior and mixed secondary information for spatial classification, and (d) develop an approach for optimizing spatial sampling schemes based on secondary information. Simple kriging with varying local means (or regression kriging) was the most robust method for incorporating secondary information in the spatial estimation of CS. On-the-go sensed apparent electrical conductivity was the most useful secondary information evaluated, providing the greatest contribution to increasing map accuracy and precision. A novel algorithm for spatial clustering utilizing mixed categorical and continuous secondary information was developed. The spatial clusters served as stratification for optimizing spatial sampling utilizing the simulated annealing algorithm. Optimization resulted in even spatial coverage of the field and provided a robust variogram estimate. Results indicate that sampling demand can be reduced if correlated secondary information is used for field stratification and spatial interpolation at unsampled locations. Future efforts should concentrate on the: (a) application of regression kriging techniques to CS mapping at different spatial scales, particularly with regard to availability and effectiveness of secondary information, (b) determination of the optimum sample sizes for a particular accuracy, precision and cost of sampling, and (c) use of secondary information for improving sampling designs for regional estimations of whole-field mean CS