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Knowing spatial patterns of functional microbial guilds can increase our understanding of the relationships between microbial community ecology and ecosystem functions. Using geostatistical modeling to map spatial patterns, we explored the distribution of the community structure, size, and activity of one functional group in N cycling, the denitrifiers, in relation to 23 soil parameters over a 44-ha farm divided into one organic and one integrated crop production system. The denitrifiers were targeted by the nirS and nirK genes that encode the two mutually exclusive types of nitrite reductases, the cd1 heme-type and copper reductases, respectively. The spatial pattern of the denitrification activity genes was reflected by the maps of the abundances of nir genes. For the community structure, only the maps of the nirS community were related to the activity. The activity was correlated with nitrate and dissolved organic nitrogen and carbon, whereas the gene pools for denitrification, in terms of size and composition, were influenced by the soil structure. For the nirS community, pH and soil nutrients were also important in shaping the community. The only unique parameter related to the nirK community was the soil Cu content. However, the spatial pattern of the nirK denitrifiers corresponded to the division of the farm into the two cropping systems. The different community patterns, together with the spatial distribution of the nirS/nirK abundance ratio, suggest habitat selection on the nirS- and nirK-type denitrifiers. Our findings constitute a first step in identifying niches for denitrifiers at scales relevant to land management.
Soil microorganisms are abundant and diverse (46), drive key processes in biogeochemical cycles, and, thus, play crucial roles in ecosystem functioning (2). They are not randomly distributed but exhibit spatial patterns at different scales (26). Spatial patterns ranging from the micrometer up to the meter scale have been reported (19, 20, 32, 37), and an understanding of such patterns can give clues to how microbial communities are generated and maintained (17). Spatial patterns of microorganisms at the field and landscape scales warrant special attention, since they could be associated with land use and aid in creating knowledge-based management strategies for agricultural production (5, 42). However, our understanding of key habitat-selective factors is limited, and few studies have specified which factors influence the spatial patterns of soil microbial communities at larger scales. Lauber et al. (30) recently demonstrated that pH could predict the community composition of soil bacteria at the continental scale. The importance of pH as a key edaphic driver of bacterial community structure has also been shown in other studies (11, 47). Another major, but complex, factor pointed out in a few studies is the soil type (4, 5, 14). Most studies have included only a limited number of properties that are easy to measure; most often, carbon and nitrogen pools and soil physical factors have been neglected in microbial community ecology. Since these factors delineate soil oxygen and water content, they may exert a stronger impact on microbial communities than other soil resources.
Reports on the field or landscape scale spatial distribution of soil bacteria have had a taxon-centered perspective at either the species or total-community level, but there is emerging interest in the biogeography of functional traits possessed by microorganisms (18). Bacterial species composition is likely important for soil ecosystem functions, but species affiliation rarely predicts in which way. In addition, the fuzzy species concept of bacteria makes it all the more difficult to link species to niches. Analysis of functional guilds, i.e., assemblages of populations sharing certain traits, can bridge this gap, and one guild of global concern that has been suggested and recently used as a model in functional ecology is the denitrifiers (39, 49). Denitrification is an anaerobic respiration pathway during which NO3− is reduced to N2 by a wide range of unrelated taxa. The process is an essential route for N loss from agricultural soil and a major source of the greenhouse gas N2O. It was recently shown that the spatial distribution of the relative abundances of denitrifiers with the genetic capacity to perform the last step in the denitrification pathway, reduction of N2O to N2, is linked to areas with high denitrification rates and low N2O emissions (38). Adding field scale predicted patterns of the denitrifier community structure to the abundance and activity would not only give insight into the mechanisms shaping the community, but also deepen our understanding of the relationships between the ecology of denitrifiers, N loss, and the agroecosystems' impact on climate change.
We hypothesize that spatial autocorrelations of the structures, sizes, and activities of communities of denitrifying bacteria is governed by soil-based resources at a scale compatible with land management. To test this, and to elucidate the effects of crop production systems and the importance of soil physical and chemical factors in the denitrifying community, we explored the spatial distribution of community structure, size, and activity in relation to 23 soil parameters at a 44-ha farm divided into one organic and one integrated crop production system. The denitrifier community was described in terms of the signature genes that encode the two different types of nitrite reductases in the denitrification pathway, the cd1 heme-type reductase (NirS), encoded by the nirS gene, and the copper oxidoreductase (NirK), encoded by nirK. The spatial patterns were mapped by geostatistical modeling, and correlation structures were explored.
The Logården research farm in Sweden (58°20′N, 12°38′E; altitude, 50 m) has evaluated integrated and organic farming since 1991 (24). Each system has an individual 7-year crop rotation, and green manure leys are used in both. The integrated farming system (26 ha) is managed by optimizing inputs, while the organic farming system (18 ha) is managed according to the Swedish criteria for organic farming (http://www.krav.se). This includes no use of pesticides and mineral fertilizers. Since there are no animals at the Logården farm, the nitrogen supply in the organic system depends entirely on symbiotic N fixation, achieved by cultivating leguminous plants. Each farming system has fixed sampling locations, 51 in total, used in a monitoring program (Fig. (Fig.1).1). For analysis of the size, structure, and activity of the denitrifying community and soil chemical parameters, 12 soil cores (20-mm diameter) were sampled at 10-cm depth at each of the 51 fixed locations in April 2007, pooled, and sieved (4 mm) prior to analysis. Physical parameters were determined in undisturbed soil cores sampled with a steel cylinder (72-mm inner diameter; 50 mm high) at each location, whereas those that needed to be measured in situ (soil penetration resistance and surface water infiltration) were determined at each sampling location in spring 2003 (see Table S1 in the supplemental material). The clay content of the topsoil was previously predicted by Wetterlind et al. (48) using near-infrared determinations calibrated from the samples analyzed for particle size. The soil type varies within the site from a silty clay loam to silty clay soil of postglacial origin, with an average clay content of about 40% and an organic matter content of 2 to 3%.
Total nitrogen was measured as Kjeldahl-N (ISO 13878) and NO3-N, and NH4-N was extracted with 2 M KCl and determined by flow injector analysis. Organic carbon was determined by dry combustion (ISO 10694). Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) were extracted from 5 g of soil by adding 500 ml distilled water, shaking the mixture for 2 h, centrifugation at 2,500 rpm for 20 min, and filtration (0.45 μm). The DOC was determined with a TOC Analyzer (Teledyne Tekmar, OH), and DON was calculated as the difference between Kjeldahl-N and NH4-N. pH was defined in 0.01 M CaCl2 (ISO 10390). Ammonium acetate lactate (AL)-extractable P (P-AL), K (K-AL), Mg (Mg-AL), and Ca (Ca-AL) and HCl-extractable Cu (Cu-HCl) were determined in the topsoil according to the method of Egnér et al. (9).
Determinations of soil penetration resistances (PEN) at different soil depths were carried out in situ with an SP1000 Bush soil penetrometer (Findlay, Irvine, United Kingdom) with a cone diameter of 10.1 mm according to the method of Anderson et al. (1) at 10 places at each sampling location within a 10-m radius. In situ soil surface water infiltration (cm h−1) was measured at two places at each location using two steel cylinders, an inner cylinder with 36-cm diameter and an outer cylinder with 57-cm diameter. The cylinders were installed at 10-cm depth after the upper 3 to 4 cm of soil and the plant ground cover were removed, and the volumes within and between the cylinders were filled with water up to 10 cm above the soil surface. Measurements of the distance from the cylinder top to the water surface in the inner cylinder were conducted after 5, 10, 15, 20, 40, and 55 min. The water between the cylinders was kept at a water head similar to that in the inner cylinder during measurements. The infiltration (conductivity [K]) was calculated as shown in equation 1:
where q is the measured in situ flow (cm h−1), dψ is the difference in pressure head between the water surface and the lower cylinder edge (cm), and dz is the distance between the soil surface and the lower cylinder edge (10 cm). At the start, dψ was 20 cm, and the average dψ determined during measurements was used in equation 1.
On undisturbed soil cores at different depths, the water-holding capacity (WHC) was determined at 0.5 and 5 kPa matrix (adhesive intermolecular forces between the water and the solid soil particles) tension according to the hanging water column method (25). The dry-bulk density and water content at sampling were determined for all soil samples by drying them for 72 h at 105°C. The pore volume was calculated from the dry-bulk density and particle density of the soil samples.
Potential denitrification rates were determined in triplicate for each sample using the acetylene inhibition technique of Enwall et al. (11). To 25 g of soil in a 250-ml glass flask, 25 ml substrate was added to reach a final concentration of 1 mM glucose and 1 mM KNO3. The atmosphere was changed to nitrogen gas, and acetylene was added to reach 0.1-atm partial pressure. The soil slurries were incubated on a rotary shaker at 25°C for 3 h, during which time gas samples of 0.5 ml were collected every 30 min and analyzed for nitrous oxide using a gas chromatograph equipped with a 63Ni electron capture detector (Clarus 500 GC; Perkin Elmer, CT). Nitrogen gas was used as the carrier, and the gases were separated in an Elite-Plot Q column (length, 30 m; inner diameter, 0.53 mm; Perkin Elmer). The rate of N2O formation was determined by nonlinear regression.
Total DNA was extracted from 500 mg of each soil sample using the FastDNA Spin Kit for soil (BIO101, Vista, CA) according to the manufacturer's instructions. For both terminal restriction fragment length polymorphism (T-RFLP) and quantitative PCR, the primers Cd3aF/R3Cd for nirS (45) and F1aCu/R3Cu for nirK (22) were used.
For T-RFLP, all forward primers were hexachlorofluorescein labeled, and amplification was performed in triplicate in 25-μl reaction mixtures with 20 ng DNA, but otherwise according to the method of Throbäck et al. (45). The triplicate PCR products for each gene and sample were pooled, divided into three portions, and digested for 2 h according to the instructions provided by the manufacturer (New England BioLabs, Ipswich, MA) in separate reactions with 10 U of the restriction enzymes BstUI, HhaI, and HaeIII for nirS and DpnI, HpyCH4IV, and Sau96I for nirK. The terminal restriction fragments (TRFs) were separated and detected using an ABI 3730 capillary sequencer (Applied Biosystems, Foster City, CA) run at 15 kV for 40 min, and the sizes of the TRFs were determined by comparison with the internal GS-500 ROX size standard. Before injection, 1.4 μl of the digested DNA was denatured using 10 μl formamide. The T-RFLP patterns were evaluated using the Peak Scanner Software (Applied Biosystems), and TRFs of <50 bp or that contributed <0.5% of the total signal were excluded. The T-RFLP profile for each restriction enzyme consisted of three to seven dominant and several minor peaks. For the nirS gene, the average numbers of TRFs we obtained were 22, 26, and 20 with BstUI, HhaI, and HaeIII, respectively, and for the nirK gene, they were 18, 25, and 20 with DpnI, HpyCH4IV, and Sau96I, respectively.
The quantitative PCR of nirS and nirK was performed in triplicate with a total volume of 25 μl, using a DyNAmo Flash SYBR green qPCR Kit (Finnzymes Oy, Espoo, Finland), 0.2 μM each primer, and 20 ng of soil DNA with a Bio-Rad IQ5 thermal cycler (Bio-Rad Laboratories Inc., Hercules, CA). For nirS and nirK reactions, bovine serum albumin was added to reach final concentrations of 1,000 and 400 ng μl−1, respectively. The nirS and nirK fragments were amplified with an initial denaturation of the DNA at 95°C for 7 min, followed by 40 cycles of 10 s at 95°C, 30 s at 59°C, and 30 s at 72°C. For nirS, there was a data acquisition step at 88°C, and for nirK, at 80°C. All reactions ended with a melting curve starting at 75 to 77°C with increases of 0.5°C up to 95°C. Standard curves were obtained using serial dilutions of linearized plasmids containing nirS and nirK genes amplified from Pseudomonas stutzeri (ATCC 14405) and Blastobacter denitrificans (DSM 1113), respectively. Controls without templates resulted in undetectable values in all samples. Inhibitory effects on PCR performance were tested for all samples by running a PCR with a known amount of circular plasmid mixed with a known amount of soil DNA, as well as samples with a known amount of circular plasmid mixed with water. The measured cycle threshold (CT) values for the different samples were compared with those measured for the controls with water, and there were no differences in CT values.
Pairwise relationships between the soil parameters, potential denitrification activities, and gene copy numbers of the nirS and nirK genes were explored with Pearson correlations, using XLSTAT version 2008.6.07 (Addinsoft, NY). Data that were not normally distributed were first Box-Cox (power transformation) transformed. Relationships between the differences in the compositions of the nirS and nirK communities and differences in single variables (soil parameters, potential denitrification activities, and gene copy numbers of the nirS and nirK genes) among the locations were determined by correlating dissimilarity matrices generated by the Bray-Curtis distance measure. For this purpose, the Mantel test was used with Monte Carlo simulations (999 randomizations) to test for the null hypothesis using PC-ORD (version 5.10; MjM Software, Gleneden Beach, OR).
For graphical representation of community relationships among samples, the TRF profiles of nirS and nirK were analyzed by nonmetric multidimensional scaling (NMS) with the same distance matrices as for the Mantel tests. This ordination method avoids assumptions of linear relationships. The NMS was constrained to two axes with a random starting configuration for 250 iterations using PC-ORD version 5.10 (MjM Software, Gleneden Beach, OR). The stability of the ordination was determined using a Markov chain Monte Carlo method, with the instability criterion set at 0.00001, performed on 200 runs with the real data and 200 runs with randomized data to test for the null hypothesis. The ordination was rotated to maximize variance as explained by Grace (16). To search for soil factors that might influence the denitrifier community composition among samples, the chemical, physical, and biological variables were incorporated into the analysis through the use of biplot ordinations, where variables were combined into a secondary matrix and correlated with the NMS axes. The correlations between variables and NMS axes were shown as vectors indicating the direction and strength of the correlation. Each parameter in the second matrix was relativized by the column total. Sample no. 17 in the integrated field (Fig. (Fig.1)1) was omitted from the NMS analysis, since several variables were missing. Permutation tests (n = 1,000) were performed to determine the significance of vector fits with NMS axes using the vegan package (34) in the R statistical programming environment (27).
Variables in the geostatistical modeling of community structure were obtained by using the sample score for the first axis in the NMS analyses of the T-RFLP profiles of the two denitrification genes. The software GS+ (Gamma Design Software, Plainwell, MI) was used for the variogram analysis. A maximum lag distance of 450 m was used due to the size of the studied area. The small number of observations did not allow a test of the presence of anisotropy. The variograms were evaluated by cross-validation by removal of one observation at a time, which was then estimated using the selected model and the remaining observations. The mean error of deviation, the root mean square error of deviation (RMSED), and the ratio of performance of deviation (RPD) (the standard deviation [SD] divided by the RMSED) were calculated for each parameter (see Table S2 in the supplemental material). Ordinary kriging (a technique for spatial estimation through which the selection of weights is made such that the estimation variance is minimized) and the geographic information system ArcGIS 9.1 with the extension Geostatistical Analyst were used for interpolation and mapping. A circular search neighborhood with a distance equal to the range from the variogram model was used, and a maximum of 10 observations were included in the interpolation.
Denitrifying community structure, size, and activity were not randomly distributed but rather exhibited spatial autocorrelations, i.e., locations in proximity were more similar than locations further apart, at the farm scale (see Table S2 in the supplemental material). In spite of the relatively few sampling points (n = 51), indicative spatial correlation structures were observed. The shortest distance of separation between observation points was 40 m. The experimental variograms for all variables could be described by a spherical model, with a low nugget in relation to the distances beyond the range of the variogram, which varied between 167 and 450 m (see Table S2 in the supplemental material).
A patchy pattern of potential denitrification rates was observed ranging from 2.4 to 20.3 ng N2O-N g (dry weight [dw]) soil−1 min−1 with major hot spots in the southern part of the integrated farming system and in the central part of the farm bridging the two crop production systems (Fig. (Fig.2a;2a; see Table S1 in the supplemental material). Concerning the community size, the abundances of the nirS communities were in the same range as the nirK communities and varied within 1 order of magnitude among sampling locations (Fig. 2b and c; see Table S1 in the supplemental material). Similar to the map of the potential rates, the size of the nirS community displayed a patchy pattern across the entire farm (Fig. (Fig.2b).2b). In contrast, the abundance of nirK genes was in general lower in the organic system than in the integrated system (Fig. (Fig.2c).2c). In the former, the nirK community size was also more evenly distributed in comparison to the higher degree of spatial variation observed in the integrated system (Fig. (Fig.2c).2c). The spatial pattern of the sum of nirS and nirK gene copy numbers, reflecting the total abundance of nitrite reductase genes (Fig. (Fig.2d),2d), largely resembled the pattern of the nirS gene abundance (Fig. (Fig.2b).2b). Despite differences in spatial distribution patterns, the abundances of the nirS and nirK genes were significantly correlated, and both genes, as well as the sum of nirS and nirK gene copy numbers, were significantly correlated with the potential denitrification activity, all with similar r values (Table (Table1).1). The ratio between the abundances of the nirS and nirK genotypes varied between 0.4 and 5.9 and was also spatially autocorrelated (Fig. (Fig.3a).3a). In the integrated system, the ratio was around 1, except for certain areas. In these patches, either nirS or nirK dominated, indicating niche differentiation between the two different types of nitrite reductases. The organic crop production system was dominated by nirS genotypes, and the spatial variation observed was mainly due to variation in the nirS gene copy number in this system, since the nirK genes were evenly distributed here.
The kriged maps of the community structure of denitrifiers revealed that the distributions of both the nirS- and nirK-type communities displayed spatial autocorrelations, although the patterns differed from each other (Fig. 3b and c). For the nirK denitrifiers, one community type dominated in the organic crop production systems and another in the integrated system. The spatial pattern of the nirS community structure resembled that of the nirK community except for the two southern fields in the integrated system (sampling locations 1 to 8), so that the nirS community pattern was in agreement with that of the potential rates. This was confirmed by the Mantel tests, as well as the permutation tests of vector fits in the NMS, which showed that it was only the nirS community structure that demonstrated a relationship to dentrification activity expressed either per gram of soil or per gram of soil carbon (Table (Table11).
We measured 23 soil chemical and physical parameters (see Table S1 in the supplemental material). The potential denitrification activity was positively correlated with the soil NO3− concentration, DON, DOC, and clay content (Table (Table1).1). None of these parameters was correlated significantly with the nirS and nirK gene copy numbers. Instead, the gene copy numbers were significantly and negatively correlated with some of the physical parameters, indicating a relationship between gene abundance and soil structure (Table (Table1).The1).The soil Cu content was positively correlated with gene abundances, especially nirK, but easily extractable P and K were correlated negatively with the abundance of denitrification genes. The correlations between the nirS/nirK abundance ratio and soil parameters showed differential responses of the two genotypes, with the parameters describing soil structure correlated positively with the ratio and the macronutrients and Cu correlated negatively.
When we explored which soil parameters were correlated with the community structure of the nirS- and nirK-type denitrifers, 17 of the 23 soil parameters proved to be significant for either the nirS or nirK denitrifier community or both (Table (Table11 and Fig. Fig.4).4). The structures of the nirS- and nirK-type communities were mainly related to the soil physical parameters. The Mantel tests between dissimilarity matrices and the permutation tests of vector fit to the ordination gave, by and large, the same results (Table (Table1).1). Both tests showed that the soil WHC, penetration resistance, and soil dry weight influenced the communities. Other important factors were the contents of easily extractable P and K in the soil independent of the target gene (Table (Table1).1). It was only the nirS community structure that was correlated with the total N and NO3− concentrations in the soil, as well as the clay content, pH, and Ca. In addition, the nirS community showed stronger correlations with the other macronutrients than the nirK community. We found no parameter that was unique to the nirK community, although the correlations with the physical parameters were stronger for the nirK than for the nirS community structure (Table (Table1).1). As for the nirK gene copy number, the nirK community structure also seemed to be more strongly related to the soil Cu content than the nirS community was.
Most microbial processes in soil are known to exhibit spatial variation at field scale (35). This has also been demonstrated for denitrification in the pioneering work of Robertson et al. (41), followed by others (13, 38), and was confirmed in our study. Not surprisingly, soil nitrate and dissolved organic carbon were the most important factors correlated with the activity, which is in agreement with what others have found (40). The spatial pattern of the activity was reflected in the maps of the nirS community structure, but not in that of the nirK-harboring denitrifiers. Soil nitrate, dissolved organic carbon, and clay content were the three soil parameters that were correlated significantly with both denitrification activity and the separation of samples based on the nirS community structure. Of these parameters, soil nitrate and clay were unique drivers for the nirS community structure. The poor link between the nirK community structure and denitrification activity could potentially be due to the fact that currently available nirK primers do not target the genes encoding the other NirK structural type (10). However, most studies have not been able to link the structures and functions of denitrifiers in soil. Instead, recent studies have shown that denitrification enzyme activity is correlated with the size of the denitrifier community (21, 38). In accord with this, both the nirS and nirK gene copy numbers were significantly correlated with the potential rates in this study, although the spatial pattern observed for nirS gene abundance was more similar than that of nirK to the spatial pattern of the potential rates. Gene abundance is not likely to be completely correlated with potential activity, which reflects the readily activated enzymes in the soil, since the genetic pool only partly contributes to the activity at a given time point. Because denitrifiers are facultative, they also proliferate under aerobic conditions. Thus, the abundance and composition patterns observed may also be blended by the result of environmental controls acting on other traits that the denitrifying bacteria possess.
The spatial distribution of the nirS/nirK ratio, in combination with the different spatial patterns of the nirK and nirS community structures, suggest habitat selection on the two types of denitrifiers or the different Nir types these genes code for. Denitrifying bacteria possess either NirS or NirK, but not both (28, 50), and experiments have shown both reductases to be functionally redundant, as one nir gene in a denitrifying organism can be eliminated and replaced by the other type (15). The gene abundances of both nirS and nirK were, by and large, controlled by the same soil parameters, but when the nirS/nirK ratio was correlated with the soil parameters, it was shown that the soil properties exerted differential effects on the two genes. An advantage of relating the ratios over abundance to soil parameters is that they are not affected by variation in the amount of starting material between samples and therefore do not have DNA extraction bias. For example, even though both are favored by the soil copper content, the correlation with the nirS/nirK ratio was negative, indicating that copper is a stronger driver for the nirK-type denitrifiers. This fits with NirK being a multicopper protein. Soil structure also had a greater impact on controlling nirK than nirS abundance. In agreement with our speculations on niche differentiation, Philippot et al. (38) showed spatial heterogeneity of the nirS/nirK gene abundance ratios in a pasture with differences in cattle impact, and Hallin et al. (21) reported differences in ratios among treatments in a fertilization experiment. The spatial distribution of nirS and nirK gene abundance was also shown to differ in a stream reach, reflecting different habitat preferences (29). Studies of changes in denitrifier community composition and diversity support the idea of niche differentiation between bacteria with one or the other Nir type (23, 33, 43). Habitat partitioning cannot be explained only by differences in species composition, because the Nir type is not truly coupled with taxonomic affiliation (8, 28). Horizontal gene transfer and other evolutionary events have uncoupled nirK and, to a lesser degree, nirS gene phylogeny from that of the species (28). Our findings imply that the nirS/nirK ratio reflects differences in community functioning. If this is true, the two types of Nir are not ecologically redundant even though they perform the same function. This could explain the maintenance of two Nir systems throughout bacterial evolution.
The spatial distributions of the nirS and nirK genotypes were related to soil habitat in ways that suggest that niche-based processes, such as environmental filtering and competition, play roles in the structuring of both communities. However, since the community structure on the basis of soil DNA provides a historical perspective on the soil community, resource-based effects can originate either from the soil type or from management practices that have a strong impact on soil properties (14). We found that the different communities were determined by various soil factors, some of which seemed to be controlled by the farming history. For example, the spatial pattern of the nirK-harboring community corresponded to the division of the farm into the two cropping systems, with one nirK genotype dominating in the organically farmed system and another in the integrated system. This was linked to differences in soil physical parameters indicative of differences in soil structure, with a denser structure in the integrated system. The observed differences in community composition could therefore be due to differences between the crop production systems in tillage regimes, which are known to impact soil structure (6, 44) and which have been shown to affect the bacterial community composition (7, 12). The different crop rotations applied in the two systems could also have an effect, since the plant species or type has been shown to influence the composition of denitrifiers in the soil (3, 36). In agreement with this, in long-term field sites where the soil has the same geological origin, land management regimes have been shown to alter the community structure of soil bacteria (4, 37), as well as of denitrifying bacteria (11, 38). For the nirS-type denitrifiers, additional soil factors that could not be related to land management, but rather represent inherent soil properties, were also important in shaping the community. This further supports niche differentiation between nirK- and nirS-type denitrifiers, and it is likely that different processes underlie the community assembly of each type of denitrifier.
We have demonstrated that spatial patterns of community composition and abundances of denitrifying bacteria can be detected at the farm scale. These findings constitute a first step in identifying the resource-based niches for denitrifiers at scales relevant for land management and agricultural practices. That spatial patterns of denitrifying communities are found at this scale does not rule out other scales being relevant in microbial ecology. Thus, spatial patterns observed at any scale are the sum of all the underlying patterns at smaller scales (31). Future work should focus on bridging these scales and search for nested scales of spatial structures, taking full advantage of contemporary molecular methods to increase our understanding of how community assembly at the micrometer scale is related to patterns observed at the landscape scale.
This work was supported by Formas (contract 2005-1623), the Formas-financed Uppsala Microbiomics Center (contract 2005-246), and the Swedish Farmers' Foundation for Agricultural Research.
We thank C. M. Jones for help with the permutation tests.
Published ahead of print on 29 January 2010.
†Supplemental material for this article may be found at http://aem.asm.org/.