Environmental similarity is more important than distance in the community structuring processes of ammonia-oxidizing archaea in agricultural soils

Dorsaf KERFAHI,Yu SHI,Baozhan WANG,Hokyung SONG,Haiyan CHU and Jonathan M.ADAMS

1School of Natural Sciences,Department of Biological Sciences,Keimyung University,Daegu 42601(Korea)

2State KeyLaboratoryof Crop Stress Adaptation and Improvement,School of Life Sciences,Henan University,Kaifeng 475001(China)

3State KeyLaboratoryof Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academyof Sciences,Nanjing 210008(China)

4KeyLab of Microbiologyfor Agricultural Environment,Ministryof Agriculture,College of Life Sciences,Nanjing Agricultural University,Nanjing 210095(China)

5Department of Earth and Environmental Sciences,The Universityof Manchester,Manchester M13 9PL(UK)

6Universityof Chinese Academyof Sciences,Beijing 101408(China)

7School of Geographyand Oceanography,Nanjing University,Nanjing 210008(China)

ABSTRACT Ammonia-oxidizing archaea(AOA)are important in converting ammonia into nitrate in soils.While many aspects of their community structure have been studied,the relative importance of stochastic versus deterministic processes has poorly been understood.We compared AOA communities across the North China Plain,targeting the amoA gene.A phylogenetic null modelling approach was used to calculate the beta nearest taxon index to quantify the influence of stochastic and deterministic processes.We found that spatial distance between samples predicted the perceived processes involved in community structuring,with stochastic processes dominating at local scales.At greater distances,stochasticity became weaker.However,soil pH,which was also the strongest determinant of AOA community,was a much stronger predictor of community structuring,leaving the distance effect redundant as an explanation of community structuring processes.The communities of AOA differing by less than 1 pH unit differed mainly stochastically in terms of operational taxonomic unit composition.At larger pH differences,deterministic processes based on heterogeneous selection between clades became increasingly dominant.It appears that AOA community composition is largely determined by the environment.However,very similar pH environments are the exception.In environments with very close pH values,stochastic effects dominantly cause differences in community composition,whether spatially near or far.

KeyWords: amoA gene,ammonia-oxidizing bacteria,assembly processes,beta nearest taxon index,community structure,operational taxonomic unit

INTRODUCTION

Nitrification is the oxidation of ammonia (NH3) and ammonium (NH+4) to nitrite (NO-2) and nitrate (NO-3)by autotrophic prokaryotes for energy and carbon (C)(Lehtovirta-Morley,2018).Soil nitrification rate is important in agricultural production,as it indicates the conversion rate of ammonium fertilizer to nitrate,which is more available for crops due to its higher mobility in soil(Wardet al.,2011).However,nitrification often occurs at higher rates than desired in agricultural soils(Lehtovirta-Morley,2018),leading to runoffand leaching losses and pollution of waterways and groundwater (Wardet al., 2011).In addition, nitrous oxide(N2O),a greenhouse gas,is produced during denitrification(Stein,2011).Therefore,nitrification in agricultural soils is also relevant to global change.Ammonia-oxidizing archaea(AOA)are one of the major groups of nitrifiers in both natural and agricultural soils (Knnekeet al., 2005).They produce NO-2as the end product of nitrification,which is then oxidized by nitrite-oxidizing bacteria to NO-3.All known AOA belong to the phylum Thaumarchaeota,most of which are believed to grow and develop mainly by NH3oxidation(Stahl and de la Torre,2012).

There has been a great deal of research on the ecological niches and diversity patterns of AOA(Wessnet al.,2011;Zhanget al.,2012;Huet al.,2013,2015;Jianget al.,2015;Tripathiet al.,2015;Luet al.,2017;Alveset al.,2018;Xiaoet al.,2020).Soil pH,climate,and soil C and nitrogen(N)contents have been found to affect the taxonomic composition of AOA communities.It seems that AOA are more diverse in high-pH soils than in low-pH soils(Gubry-Ranginet al.,2011; Huet al., 2013).Compared to ammonia-oxidizing bacteria(AOB),AOA are thought to be more abundant(in terms of cellular abundance)in soils with low concentrations of NH3(Prosser and Nicol,2008;Martens-Habbenaet al.,2015),whereas AOB tend to be more abundant in soils with high concentrations of NH3(Daiet al., 2018).Huet al.(2013) found higher AOA and AOB abundances in soils with higher pH values,with AOA abundance being higher than AOB abundance in acidic soils.In general,AOA play a more important role in nitrification in acidic and lownutrient but organic-rich soils, as well as in flooded soils poor in oxygen(Wanget al.,2015),whereas AOB are more important in neutral or alkaline and N-rich soils(Huet al.,2015; Jianget al., 2015; Daiet al., 2018; Trivediet al.,2019).Despite these general trends, there are exceptions.For example,certain clades and operational taxonomic units(OTUs)(proxy species)of AOA are more abundant in neutral or alkaline soils and thus appear specialized to these niches(Huet al.,2015;Tripathiet al.,2015).

Although studies have been conducted on the overall patterns of occurrence, abundance, and diversity of AOA in soils, there is a major knowledge gap in the ecological processes that structure AOA communities.The community structure of AOA could be shaped by competitive exclusion,which prevents taxonomically related species of nitrifiers from coexisting and leads to overdispersion of closely related species(Webbet al.,2002;Horner-Devine and Bohannan,2006).The community structure of AOA could be shaped by phylogenetic clustering as well.In this case,closely related species tend to share niches (Webbet al., 2002; Horner-Devine and Bohannan, 2006).In addition, there may be an arbitrary pattern of exclusion and co-occurrence within the community,that is,stochastic(chance)effects dominate(Stegenet al.,2012;Zhouet al.,2014).Stochastic processes could include,for example,very slow dispersal and adjustment to climate change compared to rates of evolution and extinction(Shiet al.,2018).Rapid cross-dispersal between sites could blur out any clear niche differences that might otherwise show themselves (Shiet al., 2018).Stochastic structuring could be a result of broad and overlapping niches and competitive equivalence of species,which is relatively unaffected by competitive exclusion(Shiet al., 2018).To date,there have been only a few studies on the community structuring processes of AOA that prevail in natural soils(Tripathiet al.,2015),without any study on agricultural soils or any standardized study across a clear range of scales.

As AOA play a vital role in oxidizing N in agricultural systems and affect fertilizer use efficiency,crop growth,Nrunoff, and N2O emission, understanding the influencing factors of their community composition is of practical importance.The spatial distribution of NH3-oxidizing activity in both natural and agricultural systems is often complex and challenging to explain(Banerjee and Siciliano,2012;Chenet al., 2015; Stempfhuberet al., 2016).Shiet al.(2018)found that in the North China Plain (NCP), soil archaeal OTUs(which are thought to be mainly AOA)play an important role as keystone species in soil microbial networks.Determining the influencing factors of AOA community can help to explain the variation in nitrification rate and regulate nitrification through probiotics or inhibitors.

An earlier study by Shiet al.(2018)found that bacterial community composition across the NCP showed a scaling effect on the perceived community structuring processes.At local scales, stochastic effects were found to be relatively important in terms of similarity and difference between samples,whereas at progressively broader scales,niche-based deterministic processes became more important.The objective of this study was to see whether the general rule found by Shiet al.(2018)applicable to the AOA communities at the same site.Our hypothesis was that the same fundamental combination of processes,with environmental determinism relatively more important at broad spatial scales, would apply in this instance.This is because of the truism that,in general,the larger the distance covered and compared,the more different the environment(MacArthur,1984).For any group of organisms,more distinct environments can be expected to require more distinct adaptations for physiological survival and performance in competition,leaving relatively fewer opportunities for chance mixing or lag effects to show themselves.However,given that pH is known to be important in AOA community composition and that there is a general pH difference with distance across the NCP,we also hypothesized that the spatial scaling effect on community structuring could be a result of the connection between spatial distance and environmental variations.There might be a stronger association with environmental factors when directly considered in relation to community structuring.

MATERIALS AND METHODS

Studysite and sampling

The samples collected in an earlier study on soil microbiome across the NCP were used in this study(Shiet al.,2018).The NCP is one of the most important agricultural areas in China and has been the main area of winter wheat and summer corn rotation for the past 40 years(Chenet al.,2004).This plain covers an area of approximately 410 000 km2,most of which has an altitude of below 50 m above sea level.This region is located in the East Asian Temperate Monsoon Climate zone and characterized by a hot and rainy summer and a cool and dry winter,with an average annual temperature of 8—15◦C.The average annual precipitation is 400—500 mm in the north and 750—1 000 mm in the south.Totally,40 soil samples were collected from eight sites(Linba(LB),Pingdu(PD), Shangcai (SC), Tengzhou (TZ), Taihe (TH), Zhaoxian(ZX),Xingyang(XY),and Zouping(ZP))in the main wheat-planting districts,covering an area of 300 000 km2,as described in Shiet al.(2018), from November 20—30,2014(Fig.S1 and Table SI,see Supplementary Material for Fig.S1 and Table SI).At each sampling site of<100 km2,sampling points were located approximately 3.3 km apart(Fig.S2,see Supplementary Material for Fig.S2).In addition,12 cores were taken from the 0—15 cm layer,combined as a composite sample,and stored in an ice box for further analyses.A subset of the samples representing a range of spatial distributions,climates,and soil conditions were chosen for DNA extraction.In each spatial sampling cluster,five samples were taken.Eight clusters were sampled,resulting in a total of 40 samples.

DNA extraction and sequencing

Soil DNA was extracted and purified using a Power Soil DNA kit (MO BIO, Carlsbad, USA) following the manufacturer’s instructions.Then,the extracted DNA was processed using an UltraClean DNA purification kit(MO BIO, Carlsbad, USA) and stored at-40◦C until further use.Amplicon libraries were created using the primers Arch-amoAFand Arch-amoAR, targeting the AOAamoAgene, as described by Franciset al.(2005).The forward primers were modified to contain a unique barcode at the 5′end for each sample.The polymerase chain reaction(PCR)reactions were carried out in a final volume of 50 µL,including 25µL of 2×Rapid Taq Master Mix(Vazyme,Nanjing,China),2µL of each primer(10µmol L-1),and 20 ng of template DNA.Each sample was amplified in triplicate under the following conditions: denaturation at 95◦C for 3 min, 30 cycles of denaturation at 95◦C for 15 s, annealing at 55◦C for 30 s, and extension at 72◦C for 30 s,and final extension at 72◦C for 5 min.The PCR products were purified using an AxyPrep gel extraction kit(AxyGen,Hangzhou,China),pooled in equal amounts,and sent for sequencing using the Illumina MiSeq platform(2× 300 bp) (Illumina, San Diego, USA) at Majorbio Bio-Pharm Technology Co., Ltd., Shanghai, China(Caporasoet al.,2012).The generated sequence data were deposited onto the MG-RAST server under project ID number 98865(https://www.mg-rast.org/linkin.cgi?project=mgp98865).

Soil physicochemical analysis

Soil parameters, including pH, moisture, organic C,total N,dissolved organic C and N,NH+4, NO-3, total and available potassium, and total and available phosphorus,were analyzed as described by Shiet al.(2018).

Sequence processing and bioinformatics

The“trim.SAMPLE_ID.fq”files were used to remove the primer sequences and barcodes.The sequences were then processed and aligned using the mothur platform(Schlosset al., 2009).The default settings were: kmer searching with 8mers and the Needleman-Wunsch pairwise alignment method.The VSEARCH algorithm was used to identify and remove putative chimeric sequences(Rogneset al.,2016).Sequences were classified against the Gaia DR2 database provided by Alveset al.,(2018).Then,OTUs were classified and clustered based on the OptiClust algorithm(Westcott and Schloss,2017)with a threshold of 97%sequence similarity.

The use of phylogenetic information to deduce ecological processes requires that phylogenetic distances between taxa are related to ecological niche differences(e.g.,differences in habitat requirements)(Stegenet al.,2012).Where phylogenetic distance approximates niche difference,the niches are described as having a phylogenetic signal and are phylogenetically structured(Losos,2008;Stegenet al.,2012).To characterize the phylogenetic community within each sample(at a unique point in space and time), we quantified both the mean nearest taxon distance (MNTD) and the nearest taxon index(NTI)(Webbet al.,2002).Community assembly processes were calculatedviathe NTI and beta NTI(βNTI)using the ses.mntd function in the picante package(Kembel,2010)and the null modelling approach developed by Stegenet al.(2012), respectively.The negative values of the ses.mntd output were identified as NTI values.The NTI index quantifies the number of standard deviations for the observed MNTD from the mean of the null distribution(deduced from a run of 999 randomizations).We utilized the NTI values to assess the phylogenetic community assembly at a withincommunity scale,where the clustering and overdispersion of taxa across the overall phylogeny were shown by the positive and negative values of the NTI,respectively(Horner-Devine and Bohannan,2006).For a single community,an NTI value>2 indicates that the coexisting taxa are more closely related to one another than expected by chance (showing phylogenetic clustering).An NTI value<-2 indicates that the coexisting taxa are more distantly related than expected by chance(showing phylogenetic overdispersion).A mean NTI across all communities that is significantly different from zero indicates clustering(NTI>0)or overdispersion(NTI<0)on average(Kembel,2009).An NTI value between+2 and-2 indicates that stochastic processes dominate.The parameter βNTI represents the number of standard deviations that the observed beta MNTD(βMNTD)is from the mean of the null distribution (Stegenet al., 2012, 2013).A βNTI value>2 shows a larger value than the expected phylogenetic turnover, and a βNTI value<-2 shows a smaller value than the expected phylogenetic turnover.The matrix of βNTI values are combined with Bray-Curtis-based Raup-Crick(RCbray)to evaluate the relative contributions of homogeneous selection,variable selection,dispersal limitation,homogenizing dispersal,and undominated processes in governing the assembly of the microbial community(Stegenet al.,2013).Pairwise βNTI values<-2 or>2 indicate homogeneous selection or variable selection,respectively.The values of RCbray<-0.95 or>0.95 indicate significant deviations from the null model expectation.The absolute value of βNTI(i.e.,|βNTI|)<2 with RCbray<-0.95 or>0.95 indicates a contribution of homogenizing dispersal or dispersal limitation,respectively.Otherwise(i.e.,|βNTI|<2 and|RCbray|<0.95),the shifts in community composition are undominated.

Statistical analyses

Sequence reads were subsampled into 4 807 reads per sample before calculating the diversity measures.All sequence reads clustered into 3 614 OTUs at 97%similarity.The non-metric multidimensional scaling(NMDS)plot,based on the Bray-Curtis dissimilarities between the samples calculated using the square root-transformed OTU table,was generated using the Primer-E software(Version 6,Primer-E,Plymouth, UK).An analysis of similarity was conducted to assess the difference among sites.Canonical correspondence analysis(CCA)was performed using the CANOCO 5 software with a square root-transformed response variable.Environmental variables were forward selected with 999 permutations.Measured environmental factors were normalized,and Euclidean distance was calculated between samples to examine the overall environmental distance between the samples.Mantel and partial Mantel tests were performed with 999 permutations using Pearson’s correlation to assess the weighted relationship between βNTI and soil physicochemical properties.

RESULTS

Taxonomic composition

Alveset al.(2018) described five major lineages of AOA based on the OTUs generated fromamoA:CandidatusNitrosocaldales(NC),Nitrososphaeales(NS),CandidatusNitrosotales(NT),Nitrosopumilales(NP),and a small unassigned clade designated as Incertae sedis (NT/NP).Each lineage was further divided into subclades, as indicated by Greek letters.Across the study area of the NCP, we found 3 615 OTUs(after subsampling)of AOA,representing 13 different phyla of archaea.The most abundant OTUs belonged to NS-δ (70% of total reads), followed by NSγ (10%), NS-α (10%), and NT-α (7%) (Fig.1a).The phylum- and genus-level compositions were distinct between the different spatial clusters of samples arranged by pH.The genera NS-α-3.2.1.1, NS-δ-1.Incertae_sedis.3_OTU2_unclassified,NS-δ-2.1_OTU2_unclassified,NT-α-1.1.1.1, and NS-δ-2.2.2_OTU3 were strongly associated with the low-pH sites,whereas the genera NS-γ-1.1_OTU2_unclassified,NS-δ-1.1.2.1,NS-δ-1.1.2_OTU3,and NS-δ-1.1.2_unclassified were strongly associated with the neutraland high-pH sites(Fig.1b).

Major influences on variation in taxonomic composition

The NMDS ordination showed that the community composition of AOA differed significantly(P<0.01)among soil samples from different sites(Fig.2),and AOA communities were assembled by sampling sites.A CCA analysis was performed to assess how the environmental parameters predicted the variations in AOA communities across samples(Fig.3).Thex-axis explained 17.87%of the total variation in AOA communities, whereas they-axis explained 26.80%.The CCA revealed that among the soil parameters,only pH(P<0.01), soil moisture (P< 0.01), and NH+4(P= 0.05)were significant contributors to the variations of AOA communities across the NCP soils(Fig.3 and Table SII,see Supplementary Material for Table SII),with pH being the most powerful influencing factor,contributing 16.9%of the total AOA community variation alone.

Fig.1 Relative abundances of the detected phyla(a)and dominant genera(b)of ammonia-oxidizing archaea in the soil samples collected from eight sites,Linba(LB),Pingdu(PD),Shangcai(SC),Tengzhou(TZ),Taihe(TH),Zhaoxian(ZX),Xingyang(XY),and Zouping(ZP),across the North China Plain.NT=Candidatus Nitrosotaleales;NP=Nitrosopumilales;NS=Nitrososphaeales.The Greek letters indicate subclades.OTU=operational taxonomic unit.The numbers above bars are average soil pH values at the sampling sites.

Fig.2 Non-metric multidimensional scaling(NMDS)ordination displaying the clustering of ammonia-oxidizing archaea communities among the eight sampling sites,Linba(LB),Pingdu(PD),Shangcai(SC),Tengzhou(TZ), Taihe (TH), Zhaoxian (ZX), Xingyang (XY), and Zouping (ZP),across the North China Plain.

Fig.3 Canonical correspondence analysis(CCA)plot showing the relationships between ammonia-oxidizing archaea(AOA)community composition and soil properties among the eight sampling sites, Linba (LB), Pingdu(PD),Shangcai(SC),Tengzhou(TZ),Taihe(TH),Zhaoxian(ZX),Xingyang(XY),and Zouping(ZP),across the North China Plain.Red arrows indicate significant contributors to the variations in AOA communities.EC=electrical conductivity; SM = soil moisture; OC = organic C; DOC =dissolved OC;TN=total N;DON=dissolved organic N;TP=total P;AP=available P;TK=total K;AK=available K.

Diversity

The OTU richness and Shannon index of AOA communities significantly(P<0.01)differed among different sites,with the XY,ZP,and ZX sites having higher diversity than the other sites (Fig.S3, see Supplementary Material for Fig.S3).The results of the regression analysis indicated that the number of OTUs was most closely linked to soil pH among the measured environmental factors,with a highly significant trend.Both OTU richness and Shannon diversity index were positively correlated with pH(Fig.4).

Phylogenetic null modelling

The βNTI metric calculated using a phylogenetic null modelling approach showed a clear association between the community assembly patterns perceived by the analysis and spatial distance.When comparing the AOA community composition of samples that were spatially closest together,stochastic processes dominated in structuring the community(Fig.5).No individual ecological processes dominated,and undominated processes included weak dispersal and selection,diversification,and/or drift at larger spatial scales(Stegenet al., 2015).For samples progressively further apart in spatial distance, the role of stochastic processes diminished,and the role of deterministic processes(variable selection)became more important.However,when the βNTI analysis was applied along a gradient in pH,which was the dominant factor in determining community composition,there was a much stronger relationship.When the difference in pH (∆pH) between samples was small (less than one pH unit), stochastic processes dominated.At larger ∆pH(greater than one pH unit)between samples,deterministic processes (variable selection) dominated.The results of pairwise comparisons showed that βNTI was significantly associated with ∆pH and difference in geographical distance(∆d)(Fig.6).As pH had a much stronger predictive role than spatial distance,with anR2of approximately 0.45 as opposed to 0.02,it appeared that the role of spatial distance was largely redundant and that distance was merely significant because it was partly a predictor of pH.When samples were compared within separate pH bands,it was found that,within each pH category, the AOA communities presented a more mixed picture of stochastic and deterministic processes (Fig.7).Clear distance decay in AOA community similarity was found using Mantel correlation coefficient,but environmental factors were found to be more important in influencing community assembly processes(Table I).

Fig.5 Beta nearest taxon index (βNTI) analysis showing the associations between ammonia-oxidizing archaea community assembly processes and differences in geographical distance(∆d)and pH(∆pH)between soil samples collected from eight sites across the North China Plain.The shaded areas show the 95%confidence intervals.

Fig.6 Correlations between the beta nearest taxon index(βNTI)of ammonia-oxidizing archaea community and differences in pH(∆pH)and geographical distance(∆d)between soil samples collected from eight sites across the North China Plain.

DISCUSSION

Effect of spatial scale on communitystructuring processes

The results showed that,as hypothesized,the community assembly processes at work in determining AOA community composition tended to vary with spatial scale.Stochastic processes dominated at the smallest spatial scales(1—10 km).This is in agreement with the pattern found by Shiet al.(2018)that stochasticity dominated at smaller spatial scales ranging from 150 to 900 km and determinism dominated at larger scales of> 900 km.If distance itself was the only strong predictor, it would leave open the possibility of environmental factors or legacies of past extinction and evolution, producing distinct biotas in different localities.However,as was also hypothesized,it is clear that distance itself was a weaker predictor of community structuring than environmental factors.Soil pH was a much stronger predictor of community structuring,and when there were bigger variations in soil pH,there were also more predictable differences in community structure.This demonstrates the strong role of pH in the niche partitioning of AOA.Soil pH plays a crucial role in the variation of AOA community, which has already been suggested by researchers(Huet al.,2013,2015;Liuet al.,2013;Tripathiet al.,2015).However,the findings of this study demonstrate the sheer strength of soil pH in influencing AOA ecology, with largely predictable communities occurring on a broad scale.Apparently,dispersal was not a strong limiting factor in this study.It is only when very similar pH environments are compared that the differences between them are dominated by stochastic effects,especially undominated processes.

Fig.7 Proportions of ammonia-oxidizing archaea community assembly processes within each soil pH category of the samples collected from eight sites across the North China Plain.

TABLE I Mantel and partial Mantel test results

It appears that the structuring of AOA communities is broadly an orderly process when soils of different pH values are compared,with clearly defined niches and community structure.However,stochasticity plays an important role in communities that are very similar in pH.Thus,differences in community composition between sites with similar pH levels might not be due so much to niche differences as to a mixture of effects of random extinction, slow dispersal,and excessively rapid dispersal.If individual AOA species and strains exhibit differences in NH3oxidation potential under particular circumstances, such stochastic processes might affect the NH3oxidation potential of the overall AOA community.It is known that on a local landscape scale,the NH3oxidation potential and abundance of AOA can vary considerably without any known clear environmental causes(Erguderet al.,2009).It is possible that this is also partly due to the stochastic structuring of AOA communities.Nevertheless,on the broadest environmental scale with large pH gradients,differences in the NH3oxidation rate of soils may be largely predictable.

While this study dealt with AOA,it would be interesting to explore the structure of AOB community in the same landscape to discern whether their structuring processes differ from those of AOA.Comparing the relative and absolute abundances of both AOA and AOB,using quantitative PCR or metagenomics, would be helpful in understanding the community structuring processes of NH3oxidizers in this environment,and how variations in nitrification rates could be explained by these community differences.It would also be interesting to consider whether the structuring of the whole community of soil bacteria in this environment is also dominated more by environmental factors than by distance.It is known that pH is a main factor in the community structuring of archaea(Nicolet al.,2008;Bengtsonet al.,2012;Tripathiet al., 2013, 2015).Therefore, it is expected that the role of pH will exceed that of distance in determining community structuring.

Taxonomic composition and diversityalong environmental gradients

In addition to clarifying the role of scaling in community structuring, our study also helps to further emphasize the clear distinction in broad taxonomic composition between different pH levels and different sites according to their pH levels.It is clear from the NMDS analysis that pH is the environmental factor with the greatest effect on AOA community composition across the very broad range of soil environments in the NCP.This agrees with and strengthens the results of earlier studies that revealed the dominant effect of pH on AOA community composition.Similarly,our study also reinforces the view that AOA diversity(rather than community composition) is dominated by pH, with higher pH soils having greater OTU-level AOA diversity(Gubry-Ranginet al.,2011;Huet al.,2015;Tripathiet al.,2015).

CONCLUSIONS

The most important finding of our study is that while the community structuring processes at work in determining AOA communities tend to vary with the spatial scale over which samples are compared,it is clear that the degree of environmental difference(pH,in this case)is actually the driving factor,and the effect of distance itself is redundant as an explanation.When more similar environments are compared(regardless of spatial distance),more differences in the composition and structure of microbial communities can be clarified in stochastic terms,and conversely the differences in community between more distinct environments are explicable in deterministic terms.It would be interesting to know if this principle is true for other groups of soil microbes,or even for organisms in general.It is also important to consider how the role of stochasticity in AOA community structuring continues down to very local spatial scales of meters and centimeters, which are most likely to be important in nitrification in agricultural environments.

SUPPLEMENTARY MATERIAL

Supplementary material for this article can be found in the online version.