To begin defining the key determinants that drive microbial community structure in soil, we examined 29 soil samples from four geographically distinct locations taken from the surface, vadose zone, and saturated subsurface using a small-subunit rRNA-based cloning approach. While microbial communities in low-carbon, saturated, subsurface soils showed dominance, microbial communities in low-carbon surface soils showed remarkably uniform distributions, and all species were equally abundant. Two diversity indices, the reciprocal of Simpson's index (1/D) and the log series index, effectively distinguished between the dominant and uniform diversity patterns. For example, the uniform profiles characteristic of the surface communities had diversity index values that were 2 to 3 orders of magnitude greater than those for the high-dominance, saturated, subsurface communities. In a site richer in organic carbon, microbial communities consistently exhibited the uniform distribution pattern regardless of soil water content and depth. The uniform distribution implies that competition does not shape the structure of these microbial communities. Theoretical studies based on mathematical modeling suggested that spatial isolation could limit competition in surface soils, thereby supporting the high diversity and a uniform community structure. Carbon resource heterogeneity may explain the uniform diversity patterns observed in the high-carbon samples even in the saturated zone. Very high levels of chromium contamination (e.g., >20%) in the high-organic-matter soils did not greatly reduce the diversity. Understanding mechanisms that may control community structure, such as spatial isolation, has important implications for preservation of biodiversity, management of microbial communities for bioremediation, biocontrol of root diseases, and improved soil fertility.

The hypothesis that spatial isolation is a key determinant of microbial community structure in soils was evaluated by examining the competitive dynamics of two species growing on a single resource in a uniform sand matrix under varied moisture content. One species dominated the community under highly connected, saturated treatments, suggesting that these conditions allow competitive interactions to structure the community. As moisture content decreased, however, the less competitive species became established in the community. This effect was most pronounced at a matric water potential of -0.14 MPa where estimates of final population density and species fitness were equal. A second but more closely related species pair exhibited a similar response to decreasing moisture, suggesting that the effects of spatial isolation we observed are not simply a species-pair-specific phenomenon. These findings indicate that spatial isolation, created by low moisture content, plays an important role in structuring soil microbial communities.

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Microbial community diversity and heterogeneity in saturated and unsaturated subsurface soils from Abbott's Pit in Virginia (1.57, 3.25, and 4.05 m below surface) and Dover Air Force Base in Delaware (6.00 and 7.50 m below surface) were analyzed using a culture-independent small-subunit (SSU) rRNA gene (rDNA)-based cloning approach. Four to six dominant operational taxonomic units (OTUs) were identified in 33 to 100 unique SSU rDNA clones (constituting about 40 to 50% of the total number of SSU rDNA clones in the clone library) from the saturated subsurface samples, whereas no dominant OTUs were observed in the unsaturated subsurface sample. Less than 10% of the clones among samples from different depths at the same location were identical, and the proportion of overlapping OTUs was lower for the samples that were vertically far apart than for adjacent samples. In addition, no OTUs were shared between the Abbott's Pit and Dover samples. The majority of the clones (80%) had sequences that were less than 5% different from those in the current databases. Phylogenetic analysis indicated that most of the bacterial clones were affiliated with members of the Proteobacteria family (90%), gram-positive bacteria (3%), and members of the Acidobacteria family (3%). Principal component analysis revealed that samples from different geographic locations were well separated and that samples from the same location were closely grouped together. In addition, the nonsaturated subsurface samples from Abbott's Pit clustered together and were well separated from the saturated subsurface soil sample. Finally, the overall diversity of the subsurface samples was much lower than that of the corresponding surface soil samples.

The Pearl River Delta region has experienced rapid urbanization and economic development during the past 20 years. To investigate the impacts of urbanization on regional climate, the Advanced Research core of the Weather Research and Forecasting (ARW-WRF) model is used to conduct a pair of 1-yr simulations with two different representations of urbanization. Results show that the reduction in vegetated and irrigated cropland due to urban expansion significantly modifies the near-surface temperature, humidity, wind speed, and regional precipitation, which are obtained based on the significance t test of the differences between two simulations with different urbanization representations at the 95% level. Urbanization causes the mean 2-m temperature over urbanized areas to increase in all seasons (from spring to winter: 1.78 6 0.78C, 1.48 6 0.38C, 1.38 6 0.38, and 0.98 6 0.48C, respectively) and the urban diurnal temperature range decreases in three seasons and increases in one (from spring to winter: 20.58 6 0.38C, 10.68 6 0.38C, 20.48 6 0.28C, and 20.88 6 0.28C, respectively). Urbanization reduces near-surface water vapor (1.5 g kg 21 in summer and 0.4 g kg 21 in winter), 10-m wind speed (37% independent of season), and annual total precipitation days (approximately 6-14 days). However, the total rainfall amount increases by approximately 30%, since the decrease in the number of days with light rain (8-12) is overcome by the increase in the number of days of heavy or extreme rain (3-6), suggesting that urbanization induces more heavy rain events over the urban areas. Overall, the effect of urbanization on regional climate in the Pearl River Delta is found to be significant and must be considered in any broader regional climate assessment.

The soil environment is arguably the most complex biological community because of the extremely high diversity at small scales and a chemical environment of complex and changing gradients housed in a heterogenous physical environment. Some basic important facts regarding the complexity of soil communities are presented. For a better understanding of the soil community and its activity, the role of soil matrix in structuring microbial communities, geographical characteristics of heterotrophic communities, and the application of DNA microarray technology to environmental microbiology are discussed. The highlights of soil organic matter research within the soil science community and the inter-disciplinary nature of soil science per se, is briefly reviewed and the main conclusions are outlined.

Evaluating the calculated dry deposition velocities of reactive nitrogen oxides and ozone from two community models over a temperate deciduous forest ) is very sensitive to the minimum canopy stomatal resistance (R i ) which is specified for each seasonal category assigned in WDDM. Treating Sep-Oct as autumn in WDDM for this deciduous forest site caused a large underprediction of V d (O 3 ) due to the leafless assumption in 'autumn' seasonal category for which an infinite R i was assigned. Reducing R i to a value of 70 s m À1 , the same as the default value for the summer season category, the modeled and measured V d (O 3 ) agreed reasonably well. HNO 3 was found to dominate the NO y flux during the measurement period; thus the modeled V d (NO y ) was mainly controlled by the aerodynamic and quasi-laminar sublayer resistances (R a and R b ), both being sensitive to the surface roughness length (z 0 ). Using an appropriate value for z 0 (10% of canopy height), WDDM and Noah-GEM agreed well with the observed daytime V d (NO y ). The differences in V d (HNO 3 ) between WDDM and Noah-GEM were small due to the small differences in the calculated R a and R b between the two models; however, the differences in R c of NO 2 and PAN between the two models reached a factor of 1.1e1.5, which in turn caused a factor of 1.1e1.3 differences for V d . Combining the measured concentrations and modeled V d , NO x , PAN and HNO 3 accounted for 19%, 4%, and 70% of the measured NO y fluxes, respectively.

e Emerging resistance to antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment, and the implementation of optimized infection control measures. In this study, qualitative RNA sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies, adaptive variations associated with resistance to the antibiotic classes fluoroquinolones, aminoglycosides, and ␤-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution.