Bacteria as Emerging Indicators of SoilCondition

Syrie M. Hermans,a Hannah L. Buckley,b Bradley S. Case,c Fiona Curran-Cournane,d

Matthew Taylor,e Gavin Leara

School of Biological Sciences, University of Auckland, Auckland, New Zealanda; Department of Ecology, Facultyof Agriculture and Life Sciences, Lincoln University, Lincoln, Canterbury, New Zealandb; Department ofInformatics and Enabling Technologies, Faculty of Environment, Society and Design, Lincoln University,Lincoln, Canterbury, New Zealandc; Auckland Council, Auckland, New Zealandd; Waikato Regional Council,Hamilton, New Zealande

ABSTRACT Bacterial communities are important for the health and productivity ofsoil ecosystems and have great potential as novel indicators of environmental per-turbations. To assess how they are affected by anthropogenic activity and to deter-mine their ability to provide alternative metrics of environmental health, we soughtto define which soil variables bacteria respond to across multiple soil types and landuses. We determined, through 16S rRNA gene amplicon sequencing, the composi-tion of bacterial communities in soil samples from 110 natural or human-impactedsites, located up to 300 km apart. Overall, soil bacterial communities varied more inresponse to changing soil environments than in response to changes in climate orincreasing geographic distance. We identified strong correlations between the rela-tive abundances of members of Pirellulaceae and soil pH, members of Gaiellaceaeand carbon-to-nitrogen ratios, members of Bradyrhizobium and the levels of Olsen P(a measure of plant available phosphorus), and members of Chitinophagaceae andaluminum concentrations. These relationships between specific soil attributes and in-dividual soil taxa not only highlight ecological characteristics of these organisms butalso demonstrate the ability of key bacterial taxonomic groups to reflect the impactof specific anthropogenic activities, even in comparisons of samples across largegeographic areas and diverse soil types. Overall, we provide strong evidence thatthere is scope to use relative taxon abundances as biological indicators of soil condi-tion.

IMPORTANCE The impact of land use change and management on soil microbialcommunity composition remains poorly understood. Therefore, we explored the re-lationship between a wide range of soil factors and soil bacterial community compo-sition. We included variables related to anthropogenic activity and collected samplesacross a large spatial scale to interrogate the complex relationships between variousbacterial community attributes and soil condition. We provide evidence of strong re-lationships between individual taxa and specific soil attributes even across large spa-tial scales and soil and land use types. Collectively, we were able to demonstrate thelargely untapped potential of microorganisms to indicate the condition of soil andthereby influence the way that we monitor the effects of anthropogenic activity onsoil ecosystems into the future.

KEYWORDS biogeography, biological indicator, soil health, soil microbiology

Soil bacterial communities provide a multitude of ecosystem services which directly,and indirectly, affect the overall functioning of the soil environment (1–3). This has

resulted in many studies describing variations in bacterial community composition (4,5) and functional roles (6–8); however, less effort has been invested in exploring howthis variation correlates with soil health. There is great promise for using bacterial

Received 11 October 2016 Accepted 17October 2016

Accepted manuscript posted online 28October 2016

Citation Hermans SM, Buckley HL, Case BS,Curran-Cournane F, Taylor M, Lear G. 2017.Bacteria as emerging indicators of soilcondition. Appl Environ Microbiol 83:e02826-16. https://doi.org/10.1128/AEM.02826-16.

Editor Frank E. Loeffler, University of Tennesseeand Oak Ridge National Laboratory

Copyright © 2016 American Society forMicrobiology. All Rights Reserved.

Address correspondence to Gavin Lear,g.lear@auckland.ac.nz.

MICROBIAL ECOLOGY

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community composition or the relative abundances of individual taxa as indicators ofthe state of soil environments at the regional or even continental scale. Recentadvances in next-generation sequencing technologies now make this a plausible andattractive avenue of research, leading to the proposal that bacterial community dataare capable of providing alternative metrics of environmental health and productionpotential (9). If shown to be reliable, microbial community indicators could offersignificant advantages over traditional chemical and biological measures in terms ofthe relative speed and ease of data analysis and of the minimization of site disturbanceduring sample collection (10).

For bacterial community attributes to be a viable indicator of soil condition, it isdesirable that natural spatial variations in bacterial community composition be lessthan the variation caused by anthropogenic factors. A consensus appears to haveemerged that environmental factors, rather than dispersal limitations, are dominantdrivers of bacterial community composition (5, 11). The reduced role of dispersallimitation in determining the beta-diversity of microbial communities, even acrossbroad spatial scales, is presumed to be supported by a global dispersal of microbialcells, including those carried on major atmospheric currents (12) and oceanic currents(13). While there have been numerous bacterial biogeography studies that haveemployed DNA-fingerprinting techniques over large spatial scales (4, 5), DNA-sequencing studies at similar scales are comparatively scarce. To date, most sequencingstudies have analyzed relatively small numbers of samples, environments, and landuses or only small spatial scales where the effect of dispersal limitation can already bepresumed to be minimal. More studies that simultaneously analyze large spatial scales,and a variety of soil and land use types, are required to confirm if relationshipsobserved between bacterial communities and soil environmental factors are pervasiveor if they are instead strongly mediated by geographic location. This would be the firststep toward supporting the broad-scale use of bacterial data as a viable indicator of soilhealth.

In support of their ability to indicate the condition of the soil environment, previousbiogeographic studies have identified several variables that correlate with changes insoil bacterial community composition. Most notable is the evidence that pH influencesbacterial communities at the regional (14) and continental (4, 5) scales. Other variablessuch as the carbon-to-nitrogen ratio, moisture content, and soil temperature alsocorrelate with changes in soil bacterial communities (5). However, relationships be-tween bacterial communities and critical variables associated with the nature andintensity of human land use are frequently overlooked or are studied in isolation. Theseinclude concentrations of the many heavy metals that accumulate and impact biolog-ical communities in urban settings (e.g., zinc, lead [15]) and rural settings (e.g., copper,chromium [16]), as well as core soil physical attributes, such as porosity, which cancorrelate negatively with stock density and can ultimately impact the productionpotential of agricultural land (17, 18). The pairing of large-scale surveys of soil bacterialcommunities to data gathered through long-term soil monitoring programs (5, 19, 20)provides opportunities to uncover and quantify the strength of relationships betweenbacterial communities and a much wider range of soil physicochemical variables thanhad previously been achieved. This would then define which chemical and physicalstresses on the soil environment are reliably portrayed by bacterial communities.

To date, many studies have assessed changes in bacterial communities only as awhole rather than assessing the responses of individual taxa (4, 19). Others haverestricted their analyses to investigations of changes in the most dominant phyla (21,22), although these are not necessarily the most important ones, or the only ones,driving the changes observed in the overall community (23). Assessing communityresponses at lower taxonomic levels, such as the genus level, could highlight importanttrends that might not always be observed in the higher taxonomic ranks (24). Theabsence of studies assessing the responses of important taxa on an individual basishinders not only our ability to expand our knowledge of the ecological attributes of

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these important community members but also our ability to truly assess the potentialof bacterial taxa to serve as biological indicators of ecosystem health.

By pairing bacterial community data and extensive metadata gathered from 110sites, we asked three main questions. First, is variation in bacterial communities morestrongly related to environmental changes or to the geographic distance separating thecommunities? We predict that soil bacterial communities would be more stronglycorrelated with soil environmental factors rather than with purely spatial factors. Thiswould suggest that bacterial community data may be suitable for the assessment of soilstatus across the geographic area from which samples were taken. Second, whichenvironmental variables correlate with changes in bacterial community composition?While we expect to find, consistent with other studies, that pH has a dominant effecton bacterial communities, we also anticipate that we will be able to uncover importantrelationships between bacterial community structure and other soil variables that areindicators of soil condition. Third, can the abundances of different individual taxa beused to monitor soil condition? Determining how individual taxa respond to a range ofenvironmental factors, and especially to changes in soil variables brought about byanthropogenic activity, may have important implications for how we monitor thehealth of our soils in the future.

RESULTS

After quality filtering, we obtained 5.46 million sequencing reads with an averagelength of 418 bp. After rarefying the data from each sample to 2,000 reads, weidentified 17,495 unique operational taxonomic units (OTUs) in our data set. TheseOTUs represented 56 bacterial phyla, 613 families, and 987 genera.

Relationships among spatial variables, soil factors, and bacterial communitycomposition at the regional scale. Linear regression analysis revealed that, in general,the five different categories of land uses showed similar results in terms of therelationship between bacterial community dissimilarity and geographic distance, cli-matic dissimilarity, or dissimilarity in the soil environment (Fig. 1). Notably, the exoticforest sites and dry stock sites seemed to show slightly steeper trends in assessmentsof the increase in community dissimilarity with geographic distance, while the indig-enous forest sites showed the steepest increase in community dissimilarity as the soilenvironment became more dissimilar. A significant relationship between bacterialcommunity dissimilarity and changes in climate was observed only for dairy and drystock sites, indicating that, overall, this was not an important variable affecting thebacterial communities at the scale of our sample collection. Similarly, for most of theland uses, the increase in bacterial dissimilarity with increasing geographic distance wasminimal. These results are further confirmed by the spatial pattern observed across thestudy area, where communities of bacteria with similar compositions formed clusters indifferent parts of the study area (Fig. 2) rather than a simple, distance-based gradientof increasing dissimilarity being observed. Overall, the increase in dissimilarity as thesoil environments became more dissimilar was steeper than for the other two variablesmeasured. Consistent with this, for all combined samples, soil variables explained thegreatest amount of variability in community composition (51% [see Fig. S3 in thesupplemental material] compared to the 5% explained by a combination of spatial andclimatic factors). This general trend was consistent for the compositions of phyla andclasses analyzed, with the exception of the members of the Gammaproteobacteria,which appeared to be more impacted by spatial factors (Fig. S3).

Relationship between bacterial community composition and soil parameters.Overall, pH, carbon-to-nitrogen ratio (C:N), and the level of Olsen P (a measure of plantavailable phosphorus) accounted for a larger amount of variation in bacterial commu-nity composition than any of the other variables (Fig. 3). Specifically, distance-basedmultivariate multiple regression showed that pH accounted for the greatest amount ofvariation in community composition (9.6%) but particularly with respect to the abun-dances of the members of Planctomycetes (25.2%) and candidate division WPS-2(32.4%) (Fig. 3). In contrast, more of the variations in the abundance of the members of

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FIG 1 Relationship between dissimilarity in bacterial community composition (Bray-Curtis measure) and (a)geographic distance, (b) dissimilarity in climate, and (c) dissimilarity in the soil environment, for samples withineach land use category. Linear regression lines for each trend are plotted, and adjusted R2 values are provided.Asterisks (*) indicate significant values (P value � 0.05); full regression equations are provided in Table S4.

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Proteobacteria (32.8%), Actinobacteria (37.2%), Acidobacteria (33.7%), and Firmicutes(21.2%) were explained by C:N.

Although C:N explained the largest amount of variation in the abundance ofProteobacteria, this was primarily driven by the relationship between C:N and theabundance of the Gammaproteobacteria (19.4%). Indeed, pH was still more importantin explaining the abundance of the members of the classes Betaproteobacteria (15.2%)and Deltaproteobacteria (25.2%), while Olsen P was more important in explaining theabundance of the members of the class Alphaproteobacteria (28.9%).

The majority of variation in the relative abundance of Bacteroidetes that could beaccounted for by soil factors was attributed to the concentration of aluminum (16.1%),while copper was the most important explanatory variable for Chloroflexi abundance(17.4%).

Figure 3 also shows the univariate relationships between each taxon and each soilvariable in the form of Pearson’s correlation coefficient values. Most of the bacterialphyla showed a positive relationship with pH, increasing in abundance as soils becamemore neutral (Fig. 3). The exceptions to this are the phyla Alphaproteobacteria, Acido-bacteria, and WPS-2, which all decreased in abundance as soils became less acidic.Proteobacteria, Alphaproteobacteria, Gammaproteobacteria, Acidobacteria, and WPS-2 allincreased in abundance with increasing C:N, while Betaproteobacteria, Actinobacteria,Planctomycetes, and Firmicutes all decreased in abundance with increasing C:N. Alpha-proteobacteria showed a negative correlation with the level of Olsen P in the soil, whileBetaproteobacteria, Gammaproteobacteria, Chloroflexi, and Bacteroidetes were positivelycorrelated with this variable. The relationship of Bacteroidetes with aluminum wasnegative. Conversely, Chloroflexi increased in abundance as concentrations of copperincreased.

FIG 2 Location of the sites sampled across northern New Zealand; points are colored according to theland use of each site. Variability in the composition of bacterial communities across the study region isportrayed using kriging interpolation of the first-axis nMDS scores (calculated from a Bray-Curtisdissimilarity matrix based on relative phylum abundances). The color scale on the left indicates theextrapolated scores derived from the first nMDS axis; areas with similar colors represent sample data thatclustered together on the first nMDS axis. A similar map, produced using the scores of the second nMDSaxis, is provided in Fig. S2 in the supplemental material. (Outline maps are from Statistics New Zealand[Creative Commons Attribution 4.0 International license].)

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Soil pH, C:N, and concentrations of Olsen P, aluminum, and copper were the onlyvariables that explained at least 15% of the variation in abundance of one or more taxa.Therefore, we chose to further explore correlations between these five soil attributesand the abundance of bacteria grouped to the genus level. Where possible, the genuswhich correlated most strongly with each variable, in terms of relative abundance, wasselected for further analysis (Fig. 4). However, three of the four “genera” could not beidentified beyond the family level and thus may represent groups of genera that simplyremain to be differentiated taxonomically. Copper was ultimately excluded from theanalyses as none of the groups of organisms that showed a strong relationship with thisvariable could be identified beyond the class level.

Members of the family Pirellulaceae were strongly and positively correlated with soilpH, increasing in abundance in more neutral soils (r � 0.60, P � 0.001; Fig. 4a). Therewas a strong, negative correlation between members of the family Gaiellaceae and C:N(r � �0.66, P � 0.001; Fig. 4b). The genus Bradyrhizobium, a member of the Alphapro-teobacteria, was strongly and negatively correlated with the level of Olsen P in the soil(r � �0.60, P � 0.001; Fig. 4c). Lastly, the concentrations of aluminum were negativelycorrelated with the abundance of members of the family Chitinophagaceae (r � �0.39,P � 0.001; Fig. 4d).

Relationships within individual anthropogenic land uses. If bacterial indicatorsare to be used to inform on the condition of soils under managed land uses, thensignificant relationships between key taxa and soil attributes should remain, evenwhen soils under native land use are excluded from analysis. Excluding the indig-

FIG 3 Relationship between bacterial community composition or relative taxon abundances and each soil variable. The radiusof each circle represents the amount of variation in community composition or taxon abundance that was accounted for byeach soil variable, based on adjusted R-squared values from distance-based multivariate multiple regression analyses; onlystatistically significant (P value � 0.05) contributions are shown, based on 999 permutations of the data. Additionally, theunivariate relationship between the abundance of each taxon and soil variables, calculated using Pearson’s correlationcoefficient, is represented by the color of the circle (blue represents a negative correlation; orange represents a positivecorrelation). Phyla or classes are ordered according to overall abundance in all the samples, from most abundant (top) to leastabundant (bottom). Daggers (†) indicate that some soil variables were correlated with other variables in the data set, leadingto some being removed from analysis, as detailed in Table S3. NH4-N was also included in the analysis, but the results did notreveal a significant relationship with bacterial community composition or relative taxon abundance and are therefore notshown.

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enous forest soil samples to assess only the response of taxa to soil variables in thehuman-impacted samples, all correlations were still significant. In fact, the correla-tion between aluminum and Chitinophagaceae became stronger (see Table S5 in thesupplemental material).

Horticultural soils had significantly higher soil pH than most of the other landuses, with the exception of dairy-farming sites (Fig. S4a). Despite these significantdifferences in soil pH across land uses, members of the family Pirellulaceae werecorrelated with this soil measure, even if restricting our analysis to only data fromwithin horticultural sites (r � 0.39, P � 0.025), dairy sites (r � 0.64, P � 0.001), orindigenous forest sites (r � 0.83, P � 0.001). The indigenous forest sites, on average,had higher C:N than the dairy, dry stock, or horticulture sites (Fig. S4b), but therelationship between Gaiellaceae and this variable remained significant for horti-culture soils (r � �0.46, P � 0.007), dairy soils (r � �0.57, P � 0.001), exotic forests(r � �0.90, P � 0.014), and indigenous forests (r � �0.79, P � 0.001). The genusBradyrhizobium proved to be particularly well correlated with the level of Olsen Pin horticultural soils (r � �0.54, P � 0.001), which had significantly higher levels ofOlsen P than all the other land use categories except dairy-farming sites. TheChitinophagaceae group was correlated with the concentration of aluminum withinthe land uses that differed most in terms of average soil aluminum concentrations(i.e., r � �0.45 and �0.37 and P � 0.007 and 0.02 for samples taken fromhorticultural and dairy land uses, respectively).

FIG 4 Relationships between specific soil variables and the abundances of four selected taxa. The Pearson’s correlationcoefficient (r) value for each relationship is indicated; all correlations were significant (P values � 0.001). Each point representsone site; sites are colored according to land use. Asterisks (*) indicate taxa that were classified only to the family level; therefore,these groups of organisms may consist of several genera within that family which remain unclassified. Daggers (†) indicate soilvariables that were correlated with other soil variables in the data set, which were removed. Thus, for example, relationshipsbetween the relative abundances of Bradyrhizobium and cadmium similar to those determined for Olsen P may be expected,as detailed in Table S3.

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DISCUSSION

Our study, which combined comprehensive bacterial community data with detailedsite metadata, was able to identify key relationships of bacterial communities, orindividual taxa, with their environment. Specifically, we showed that rather than purelyspatial factors, the data that correlated most closely with changes in the bacterialcommunities were the soil environment data. Importantly, we demonstrated strongtrends between individual taxa and soil variables known to be strongly influenced byanthropogenic activity. These specific relationships are significant, not only becausethey reveal interesting ecological attributes of these taxa, such as their tolerance orsensitivity to certain environmental conditions, but also because they highlight theirpotential to indicate the condition of agricultural and pastoral soils.

The results from this study show that while a small amount of the variation incommunity composition is explained purely by spatial factors, a greater portion isexplained by environmental variables, consistent with previous work (4, 19, 25). As alsoreported by Griffiths and colleagues (5), we confirmed that the soil environment is moreimportant in structuring bacterial communities than climatic variables. Overall, thisimplies that, at the spatial scale investigated here, factors such as dispersal limitation donot appear to be important for structuring bacterial communities. Furthermore, it alsosuggests that bacterial communities may respond in a somewhat predictable mannerto environmental variation brought about by land use change and management,indicating that bacterial community data may indeed be a useful tool for assessing thestatus of the soil, at least within the area from which our samples were collected.

Consistent with what has been previously reported, pH explained the greatestportion of variability in bacterial community composition in our study. The resultshighlighted that this trend not only occurs in a wide range of geographic settings butalso remains consistent even when collecting samples across larger spatial scales thannormally considered (14) or indeed when using a DNA sequencing approach instead ofDNA-fingerprinting approaches, which have dominated investigations of microbialbiogeography for many years (4, 5). Another previously reported trend that we ob-served for our samples was the relationship between bacterial communities and theratio of carbon to nitrogen in the soil (5, 26, 27).

However, as predicted, we were able to uncover relationships between the com-position of bacterial communities and important soil variables less frequently includedin investigations of soil microbial biogeography. While 14 of the 17 representative soilvariables included in the analyses were able to explain a significant portion of thevariability in community composition, the correlations with Olsen P were of particularinterest. Olsen P is strongly linked to land use, as phosphorus is an important compo-nent of the fertilizer that is applied to soils used for both horticultural and pastoralpurposes (28). Many studies investigating soil bacterial communities have not includedany measure of phosphorous in their analyses (see, e.g., references 21, 27, and 29), butlevels of Olsen P have been previously identified as influencing the composition ofbacterial communities (5). The relatively large effect observed in the present studycould be driven by the broad range of Olsen P levels recorded (1.5 to 383.5 �g/cm3),with particularly high levels reported in soils exposed to intensive anthropogenicactivity and lower values generally recorded for soils under native forests. Additionally,Olsen P, which is a measure of plant available phosphorus, was correlated with theconcentration of total phosphorus in our data set and also with the concentration ofcadmium (see Table S3 in the supplemental material). Therefore, the relationship thatbacterial communities showed with Olsen P may also apply to additional variables,which may contribute to the strong patterns observed here.

Overall, similarly to what was observed for community composition, individual taxa,at both the phylum and genus levels, showed strong relationships with variation in thesoil environment. Members of the family Gaiellaceae correlated with C:N in our samples.This poorly understood and novel family is comprised of strict aerobes and chemoor-ganotrophs (30). Members of this family have been proposed to be associated with

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plants (31), and while previous studies have found links between their abundance andsoil variables such as calcium, magnesium, and cation exchange capacity (32), correla-tions to the ratio of soil carbon to nitrogen, such as were found here, have not beenpreviously reported. This shows that an increased understanding of soil bacteria, atvarious taxonomic levels, can be obtained through large-scale studies that include awide range of soil variables.

The negative correlation between the levels of Olsen P and the abundance ofBradyrhizobium, members of which fix nitrogen both as a symbiont on legume rootsand in a free-living state, is of particular interest (33, 34). This finding is significantbecause rising concentrations of Olsen P across the study sites have been highlightedas being of recent concern (20, 35). This genus has previously been identified as beingof interest in indicating the effects of agricultural land use; its abundance has beenshown to be lower in land used for agriculture, increasing over time after the land wasretired from agriculture (34). However, while our indigenous forest soils had signifi-cantly lower levels of Olsen P and the more intensive land uses had higher levels, theobserved correlation between Bradyrhizobium and Olsen P is not simply due to thepresence or absence of anthropogenic activity. The trend remains significant even ifconsidering only the human-impacted sites. The consistent and prevalent patternspresented here suggest that monitoring this genus has potential for use as a biolog-ically relevant indicator for important soil variables such as Olsen P and the overalleffects of land management.

Another important relationship was that between heavy metals and the abundanceof several key taxa. Although heavy metals are naturally present in soils, their concen-trations are constantly being altered and are influenced by the use of fertilizers,pesticides, and wastewater irrigation, as well as through contamination from industrialareas or large residential areas (36). In the present study, data from heavy metals wereable to explain a significant portion of variability in the abundances of all the analyzedphyla and classes, with between 1 and 3 different metals correlating with any givenphylum or class. Specifically, the concentrations of aluminum were strongly related tothe abundance of members within the family Chitinophagaceae. Aluminum has beenpreviously shown to affect the diversity of bacteria in agricultural soils (37) and to belinked to changes in bacterial diversity in forest ecosystems (38). However, it isimportant that the concentrations of aluminum were correlated with the concentra-tions of several other heavy metals (Table S3). Therefore, it is possible that the membersof this taxon were responding to the presence of any of these heavy metals or, indeed,to the total heavy metal suite. Additional experimental approaches are required todistinguish these relationships. Regardless, members of the family Chitinophagaceaeappear to be good candidates as indicators of environmental perturbations and toprovide proof of concept for the use of bacterial taxa as indicators of soil status.

Overall, our results indicate that bacterial communities, and specific taxa, are indeedcapable of reflecting the changes occurring in a soil environment due to anthropogenicactivity. While, as we have shown here, this could be measured based on the directabundances of taxa known to respond to specific soil variables, there are othermethods worth exploring in future work. The use of machine learning tools such as arandom forest classifier trained on bacterial data to indicate the health state of soilenvironments would be of particular interest. Methods such as this have previouslybeen successful in creating an in situ environmental indicator that can classify siteseither as being contaminated with uranium or nitrate or as being uncontaminated (39).Although the success of such models is promising, it remains to be seen if they can beapplied to detect subtler changes induced by land use management, which wouldlikely result in weaker, and less-specific, selective forces acting on the bacterial com-munities.

To conclude, our study showed that changes in the soil environment, largelybrought about by anthropogenic activity, correlate more strongly with changes inbacterial community composition than with spatial factors. Notably, while confirmingthe relationship between bacterial community composition and soil pH and C:N, we

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also uncovered important relationships between the bacterial communities and OlsenP. Furthermore, we showed that several phyla were more strongly influenced byvariables such as Olsen P, or by the concentration of heavy metals such as aluminumand copper, than by pH. Pairing data obtained from soil monitoring programs withbacterial community data presents unique opportunities to uncover important rela-tionships between individual phyla, classes, or even genera and soil variables influ-enced by anthropogenic activity. The confirmation of a strong relationship betweenspecific taxa and anthropogenesis-related soil variables suggests that monitoring thepresence of these taxa could serve as a biologically relevant indicator of the conditionof our soils.

MATERIALS AND METHODSSample collection. We collected samples between 2013 and 2014 from 110 sites in northern New

Zealand (Fig. 2). Our sampling area covered approximately 29,500 km2 of land consisting of diverse soil types;over half of this area is used for pastoral farming and horticulture, and the remainder is covered in forest orbare rock (volcanic cones) or consists of native tussock or urban areas (40) (Waikato Regional Council, 2015[http://www.waikatoregion.govt.nz/Environment/Environmental-information/Environmental-indicators/Land-and-soil/land1-report-card/]). We classified sites to the soil order level according to the NewZealand Soil Classification (41) and the World Reference Base for Soil Resources (42). The soil orderclassifications (and equivalents in the World Reference Base [WRB]) that were included were GranularSoils (Ferralsols) (n � 23), Allophanic Soils (Andosols) (n � 25), Ultic Soils (Acrisols) (n � 17), Pumice Soils(Andosols) (n � 14), Gleys (n � 11), Organic Soils (Histosols) (n � 8), Brown Soils (Cambisols) (n � 8), andRecent Soils (Fluvisols and Arenosols) (n � 4). Sites are further categorized as being dominated byindigenous forest, exotic forest, dairy pasture, dry stock pasture, or horticulture (43) (see Table S1 in thesupplemental material).

For microbial analyses, we collected five soil cores at each site (0 to 10 cm in depth, 2.5 cm indiameter), after removing leaf litter and plant biomass, across a transect at 10-m intervals. These sampleswere kept on ice until they could be transferred to �20°C storage until further use. We composited anadditional 25 soil cores collected from the same transect at 2-m intervals for soil chemical analyses (Table1), while we took intact soil cores (0 to 9 cm in depth, 10 cm in diameter) at 15-m intervals for soilphysical analyses (Table 1) (43).

Molecular methods. Before DNA extraction, we homogenized each thawed soil sample by manualmixing. We used PowerSoil-htp 96-well DNA isolation kits (Mo Bio Laboratories Inc., CA, USA) followingthe manufacturer’s instructions but with the following minor modifications: (i) mechanical lysis wasperformed by agitating the plates in a Qiagen TissueLyser II instrument (Retch) for 4 min at a frequencyof 30 Hz; (ii) the ethanol air-drying time was extended to 15 min; and (iii) plates were incubated at roomtemperature for 5 min after elution buffer was added. In total, we extracted DNA from 550 samples,which we stored at �20°C until further analysis.

To characterize the diversity and composition of soil bacterial communities in each sample, weamplified V3/V4 regions of bacterial 16S rRNA genes from each soil extract using modifications of theprimers 341F (5=-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3=) and 785R(5=-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3=). This primer pair hasbeen demonstrated to provide good coverage for bacteria and is purposely designed for optimal use onIllumina MiSeq DNA sequencing platforms (44). The primers include the Illumina adapter sequences(underlined) that are required for downstream sequencing. We amplified DNA from each sample, as wellas mock community DNA (BEI Resources; item HM-783D), under the following amplification conditions:(i) 95°C for 3 min; (ii) 25 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s; and then (iii) 72°C for5 min. We individually purified PCR products using SequalPrep normalization plates (Invitrogen) or DNAClean & Concentrator kits (Zymo Research), per the instructions of the manufacturers. Finally, wemeasured and recorded the concentration of purified PCR products using a Qubit double-stranded DNA(dsDNA) HS assay kit (Life Technologies, USA) and normalized the concentrations where required. Theamplified material was then submitted to New Zealand Genomics Ltd. for sequencing on an IlluminaMiSeq instrument using 2-by-300-bp chemistry. Prior to DNA sequencing, the sequencing provider

TABLE 1 Groups of representative explanatory variables used to explain sources of variation in bacterial community composition and therelative abundances of selected bacterial taxaa

Explanatory group Variables included

Space NZTM Easting and NZTM NorthingClimate Annual rainfall,b solar radiation, January maximum temp, July minimum temp, elevationb

Soil pH, total carbon,b anaerobic mineralizable nitrogen (AMN), C:N, Olsen phosphorus (Olsen P),b NO3-N, NH4-N,c bulk density,macroporosity, aluminum, barium,b chromium,b copper,b magnesium,b rubidium,b strontium,b slopec

aSee Table S3 in the supplemental material for correlating explanatory variables.bVariable was log transformed before being used in analyses to reduce skewedness.cVariable was log transformed after all values were increased by a value of 1 to remove null values which cannot be log transformed.

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attached a unique combination of Nextera XT dual indices (Illumina Inc., USA) to the DNA from eachsample to allow multiplex sequencing.

Bioinformatic methods. The DNA sequence data were quality filtered, after which we picked denovo operational taxonomic units (OTUs) using USEARCH v 7.0 (45). Forward and reverse reads weremerged using the fastq_mergepairs command. We truncated reads at the first position that had a qualityscore (Q score) of less than 3 and set the minimum length of the merged read to 200 bp. We thentrimmed the first 20 bp from the start of all the merged sequences using the -fastq_filter command sincethis region had a high probability of error. Reads with two or more expected errors were discarded.Finally, we dereplicated sequence data (-derep_fulllength), removed singletons (-sortbysize), and clus-tered sequences into OTUs at 97% sequence similarity, using the UPARSE-OTU algorithm (46).

We performed taxonomic assignment within QIIME (Quantitative Insights into Microbial Ecology,version 1.8) by comparisons against the Greengenes reference database (version 13.8; 47) beforerandomly rarefying to a depth of 2,000 sequences per sample to achieve a standard sequencing “depth”across all samples.

Analysis of soil physicochemical and climatic data. We coupled our bacterial and soil physico-chemical data sets with further environmental data collated for each sampling location using ArcGIS 10.3(Environmental Systems Research Institute [ESRI], Redlands, CA). The extraction tools within the spatialanalyst toolset were used to obtain climate and site aspect data (Table S2), based on the site locationdata (New Zealand Transverse Mercator [NZTM] Eastings and Northings). When a large range of soil andclimatic variables are measured, several are usually correlated with each other, and this could haveundesirable effects for downstream analyses. We therefore identified highly correlated explanatoryvariables (with a Pearson’s correlation value greater than 0.6 [either negative or positive]) and includedonly one of the representative variables in downstream analyses. This led us to keep 22 variables (wediscarded 22 variables; Table S3). For all reported results, any significant correlation of bacterialcommunity composition, or taxon abundance, with a representative explanatory variable could equallybe caused by variability of any of the removed variables that correlated with the representative variable.

Statistical analyses. We removed 16 samples from our analysis because they had fewer than 2,000DNA sequence reads. To eliminate any biases associated with unequal levels of coverage across sites, wecalculated centroid bacterial community data for each site by taking the mean abundance value for eachOTU from three randomly selected samples on each transect. We then assessed differences in bacterialcommunity composition by calculating the Bray-Curtis dissimilarities for each pair of samples, using theaveraged OTU abundances for each site. We also assessed differences in community composition at thetaxonomic levels of phylum, class, and genus. Bray-Curtis dissimilarity matrices were generated in Rv3.2.1 using the ‘vegan’ package.

We used nonmetric multidimensional scaling (nMDS) of the Bray-Curtis dissimilarity matrix fromphylum abundances to obtain site scores in compositional space using the Primer v.7 computer program(48). Using the ArcGIS kriging function in the extrapolation toolset, we then mapped the first and secondnMDS axis scores to generate a geographical representation of the spatial patterns in communitycomposition.

We used distance decay analysis and linear regression to further investigate patterns in the bacterialcommunities for each of the five land uses separately. For this, pairwise comparisons of bacterialcommunity dissimilarities within each land use category (Bray-Curtis measures based on OTU abun-dances) were plotted against geographic distance or dissimilarity in either climatic variables or the soilphysicochemical attributes. Differences in climate and soil physicochemical attributes among sampleswere quantified by calculating the Euclidean distances among samples based on the first 10 principalcomponents for climatic variables and the first 17 principal components for soil attributes (calculatedfrom the climate and soil variable data in Table 1). Principal-component analysis could not be performedon the soil variables from the exotic forest samples, due to a shortage of sites, and therefore the changesin bacterial community composition with decreasing soil similarity were not assessed for this land use.We used linear regression to quantify the strength of the relationship between community dissimilarityand geographic distance, climate dissimilarity, or soil physicochemical attributes for each land use, wherepossible.

The three different groups of explanatory variables (space, climate, and soil; Table 1) were also usedto explain the variations in community composition and the abundances of specific phyla using variancepartitioning procedures as previously described (11). To explain variations in community compositionbased on the relative abundances of OTUs, Bray-Curtis dissimilarity matrices were used as the input datafor variance partitioning. Conversely, the abundances of specific phyla were used as univariate responsevariables, expressed as averaged abundance values at each site; the phyla selected for further investi-gation were those whose abundances had a Pearson’s correlation coefficient value greater than 0.50 withthe first two nMDS axes in analyses of all samples (Fig. S1). Due to the large number of OTUs in theProteobacteria, the four most abundant classes within this phylum were also selected for furtherinvestigation.

To explore the relationship between the soil environment and both bacterial community composi-tion and the abundance of individual taxa, we used distance-based multivariate multiple regression ofthe Bray-Curtis distance matrices. Models were built in Primer v.7 by applying a forward selectionprocedure for the soil variables using adjusted R2 values as a selection criterion. Statistical significancewas assessed by 999 permutations, and only significant variables (P value � 0.05) were included in eachmodel. Further, we determined the direction and strength of the univariate relationship betweenindividual soil variables and phylum or class abundance using Pearson’s correlation coefficient. For phylaor classes where one specific soil variable was able to explain �15% of the variation in abundance,

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Pearson’s correlation coefficient values were calculated to determine which genera within these phylashowed strong relationships with the edaphic variables. To determine if correlations between theindividual taxa and soil variables could be solely due to differences in abundance in native versushuman-impacted sites, we repeated our analyses on subsamples containing data only from human-impacted sites or only from single, anthropogenic land use types.

Accession number(s). We deposited all amplicon sequence data associated with this article in theNCBI Sequence Read Archive under accession number SRP078519.

SUPPLEMENTAL MATERIAL

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.02826-16.

TEXT S1, PDF file, 0.7 MB.

ACKNOWLEDGMENTSWe thank Emma Chibnall for her involvement in sample collection.We gratefully acknowledge the role of Auckland Council and Waikato Regional

Council in supporting our research.

REFERENCES1. Ranjard L, Poly F, Nazaret S. 2000. Monitoring complex bacterial com-

munities using culture-independent molecular techniques: applicationto soil environment. Res Microbiol 151:167–177. https://doi.org/10.1016/S0923-2508(00)00136-4.

2. Compant S, Duffy B, Nowak J, Clément C, Barka EA. 2005. Use of plantgrowth-promoting bacteria for biocontrol of plant diseases: principles,mechanisms of action, and future prospects. Appl Environ Microbiol71:4951– 4959. https://doi.org/10.1128/AEM.71.9.4951-4959.2005.

3. Wu T, Chellemi DO, Graham JH, Martin KJ, Rosskopf EN. 2008. Compar-ison of soil bacterial communities under diverse agricultural land man-agement and crop production practices. Microb Ecol 55:293–310.https://doi.org/10.1007/s00248-007-9276-4.

4. Fierer N, Jackson RB. 2006. The diversity and biogeography of soilbacterial communities. Proc Natl Acad Sci U S A 103:626 – 631. https://doi.org/10.1073/pnas.0507535103.

5. Griffiths RI, Thomson BC, James P, Bell T, Bailey M, Whiteley AS. 2011. Thebacterial biogeography of British soils. Environ Microbiol 13:1642–1654.https://doi.org/10.1111/j.1462-2920.2011.02480.x.

6. Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. 2012.Comparative metagenomic, phylogenetic and physiological analyses ofsoil microbial communities across nitrogen gradients. ISME J6:1007–1017. https://doi.org/10.1038/ismej.2011.159.

7. Fierer N, Ladau J, Clemente JC, Leff JW, Owens SM, Pollard KS, Knight R,Gilbert JA, McCulley RL. 2013. Reconstructing the microbial diversity andfunction of pre-agricultural tallgrass prairie soils in the United States.Science 342:621– 624. https://doi.org/10.1126/science.1243768.

8. Mendes LW, Tsai SM, Navarrete AA, de Hollander M, van Veen JA,Kuramae EE. 2015. Soil-borne microbiome: linking diversity to function.Microb Ecol 70:255–265. https://doi.org/10.1007/s00248-014-0559-2.

9. Lau KEM, Washington VJ, Fan V, Neale MW, Lear G, Curran J, Lewis GD.2015. A novel bacterial community index to assess stream ecologicalhealth. Freshw Biol 60:1988 –2002. https://doi.org/10.1111/fwb.12625.

10. Lear G, Dopheide A, Pierre-Yves A, Roberts K, Washington V, Smith J,Lewis GD. 2012. Biofilms in freshwater: their importance for the main-tenance and monitoring of freshwater health, p 129 –151. In Microbialbiofilms: current research and applications. Horizon Scientific Press,Norfolk, UK.

11. Lear G, Washington V, Neale M, Case B, Buckley H, Lewis G. 2013. Thebiogeography of stream bacteria. Glob Ecol Biogeogr 22:544 –554.https://doi.org/10.1111/geb.12046.

12. DeLeon-Rodriguez N, Lathem TL,Rodriguez-R LM, Barazesh JM, Ander-son BE, Beyersdorf AJ, Ziemba LD, Bergin M, Nenes A, Konstantinidis KT.2013. Microbiome of the upper troposphere: Species composition andprevalence, effects of tropical storms, and atmospheric implications.Proc Natl Acad Sci U S A 110:2575–2580. https://doi.org/10.1073/pnas.1212089110.

13. Müller AL, de Rezende JR, Hubert CRJ, Kjeldsen KU, Lagkouvardos I, BerryD, Jørgensen BB, Loy A. 2014. Endospores of thermophilic bacteria astracers of microbial dispersal by ocean currents. ISME J 8:1153–1165.https://doi.org/10.1038/ismej.2013.225.

14. Lauber CL, Strickland MS, Bradford MA, Fierer N. 2008. The influence ofsoil properties on the structure of bacterial and fungal communitiesacross land-use types. Soil Biol Biochem 40:2407–2415. https://doi.org/10.1016/j.soilbio.2008.05.021.

15. Ancion P-Y, Lear G, Lewis GD. 2010. Three common metal contaminantsof urban runoff (Zn, Cu & Pb) accumulate in freshwater biofilm andmodify embedded bacterial communities. Environ Pollut 158:2738 –2745. https://doi.org/10.1016/j.envpol.2010.04.013.

16. Tóth G, Hermann T, Da Silva MR, Montanarella L. 2016. Heavy metals inagricultural soils of the European Union with implications for foodsafety. Environ Int 88:299 –309. https://doi.org/10.1016/j.envint.2015.12.017.

17. Preem J-K, Truu J, Truu M, Mander U, Oopkaup K, Lõhmus K, HelmisaariH-S, Uri V, Zobel M. 2012. Bacterial community structure and its rela-tionship to soil physico-chemical characteristics in alder stands withdifferent management histories. Ecol Eng 49:10 –17. https://doi.org/10.1016/j.ecoleng.2012.08.034.

18. Constancias F, Prévost-Bouré NC, Terrat S, Aussems S, Nowak V, Guille-min J-P, Bonnotte A, Biju-Duval L, Navel A, Martins JM, Maron P-A,Ranjard L. 2014. Microscale evidence for a high decrease of soil bacterialdensity and diversity by cropping. Agron Sustain Dev 34:831– 840.https://doi.org/10.1007/s13593-013-0204-3.

19. Ranjard L, Dequiedt S, Chemidlin Prévost-Bouré N, Thioulouse J, SabyNPA, Lelievre M, Maron PA, Morin FE, Bispo A, Jolivet C, Arrouays D,Lemanceau P. 2013. Turnover of soil bacterial diversity driven by wide-scale environmental heterogeneity. Nat Commun 4:1434. https://doi.org/10.1038/ncomms2431.

20. Curran-Cournane F. 2015. Soil quality state and trends in New Zealand’slargest city after 15 years. Int J Environ Ecol Geol Geophys Eng9:227–234.

21. Lauber CL, Hamady M, Knight R, Fierer N. 2009. Pyrosequencing-basedassessment of soil pH as a predictor of soil bacterial community struc-ture at the continental scale. Appl Environ Microbiol 75:5111–5120.https://doi.org/10.1128/AEM.00335-09.

22. Fierer N, McCain CM, Meir P, Zimmermann M, Rapp JM, Silman MR,Knight R. 2011. Microbes do not follow the elevational diversity patternsof plants and animals. Ecology 92:797– 804.

23. Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N, GilbertJA. 2014. Conditionally rare taxa disproportionately contribute to tem-poral changes in microbial diversity. mBio 5:e01371-14. https://doi.org/10.1128/mBio.01371-14.

24. Dohrmann AB, Küting M, Jünemann S, Jaenicke S, Schlüter A, Tebbe CC.2013. Importance of rare taxa for bacterial diversity in the rhizosphere ofBt- and conventional maize varieties. ISME J 7:37– 49. https://doi.org/10.1038/ismej.2012.77.

25. Jesus EDC, Marsh TL, Tiedje JM, Moreira FMDS. 2009. Changes in landuse alter the structure of bacterial communities in Western Amazonsoils. ISME J 3:1004 –1011. https://doi.org/10.1038/ismej.2009.47.

26. Fierer N, Carney KM, Horner-Devine MC, Megonigal JP. 2009. The bio-

Hermans et al. Applied and Environmental Microbiology

January 2017 Volume 83 Issue 1 e02826-16 aem.asm.org 12

on May 29, 2020 by guest

http://aem.asm

.org/D

ownloaded from

geography of ammonia-oxidizing bacterial communities in soil. MicrobEcol 58:435– 445. https://doi.org/10.1007/s00248-009-9517-9.

27. Kuramae EE, Yergeau E, Wong LC, Pijl AS, van Veen JA, Kowalchuk GA.2012. Soil characteristics more strongly influence soil bacterial commu-nities than land-use type. FEMS Microbiol Ecol 79:12–24. https://doi.org/10.1111/j.1574-6941.2011.01192.x.

28. During C. 1984. Fertilisers and soils in New Zealand farming. Sigma PrintLimited, Petone, New Zealand.

29. Lauber CL, Ramirez KS, Aanderud Z, Lennon J, Fierer N. 2013. Temporalvariability in soil microbial communities across land-use types. ISME J7:1641–1650. https://doi.org/10.1038/ismej.2013.50.

30. Albuquerque L, da Costa MS. 2014. The family Gaiellaceae, p 357–360. InRosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (ed), Theprokaryotes. Springer, Berlin, Germany.

31. Eo J, Park K-C, Kim M-H. 2015. Plant-specific effects of sunn hemp(Crotalaria juncea) and sudex (Sorghum bicolor � Sorghum bicolor var.sudanense) on the abundance and composition of soil microbial com-munity. Agric Ecosyst Environ 213:86 –93. https://doi.org/10.1016/j.agee.2015.07.025.

32. Barbose Lima A, de Souza Cannavan F, Gomes Germano M, Dini-Andreote F, Monteiro de Paula A, Cezar Franchini J, Geraldes Teixeira W,Tsai SM. 2015. Effects of vegetation and seasonality on bacterial com-munities in Amazonian dark earth and adjacent soils. Afr J Microbiol Res9:2119 –2134. https://doi.org/10.5897/AJMR2015.7453.

33. Pagan JD, Child JJ, Scowcroft WR, Gibson AH. 1975. Nitrogen fixation byRhizobium cultured on a defined medium. Nature 256:406 – 407. https://doi.org/10.1038/256406a0.

34. Zhalnina K, de Quadros PD, Gano KA, Davis-Richardson A, Fagen JR,Brown CT, Giongo A, Drew JC, Sayavedra-Soto LA, Arp DJ, Camargo FA,Daroub SH, Clark IM, McGrath SP, Hirsch PR, Triplett EW. 2013. Ca.Nitrososphaera and Bradyrhizobium are inversely correlated and relatedto agricultural practices in long-term field experiments. Front Microbiol4:104. https://doi.org/10.3389/fmicb.2013.00104.

35. Taylor MD, Kim ND, Hill RB, Chapman R. 2010. A review of soil qualityindicators and five key issues after 12 yr soil quality monitoring in theWaikato region. Soil Use Manag 26:212–224. https://doi.org/10.1111/j.1475-2743.2010.00276.x.

36. Wuana RA, Okieimen FE. 2011. Heavy metals in contaminated soils: areview of sources, chemistry, risks and best available strategies forremediation. ISRN Ecol 2011:e402647. https://doi.org/10.5402/2011/402647.

37. Da Mota FF, Gomes EA, Marriel IE, Paiva E, Seldin L. 2008. Bacterial and

fungal communities in bulk soil and rhizospheres of aluminum-tolerantand aluminum-sensitive maize (Zea mays L.) lines cultivated in unlimedand limed cerrado soil. J Microbiol Biotechnol 18:805– 814.

38. Faoro H, Alves AC, Souza EM, Rigo LU, Cruz LM, Al-Janabi SM, MonteiroRA, Baura VA, Pedrosa FO. 2010. Influence of soil characteristics on thediversity of bacteria in the southern Brazilian Atlantic forest. Appl Envi-ron Microbiol 76:4744 – 4749. https://doi.org/10.1128/AEM.03025-09.

39. Smith MB, Rocha AM, Smillie CS, Olesen SW, Paradis C, Wu L, CampbellJH, Fortney JL, Mehlhorn TL, Lowe KA, Earles JE, Phillips J, TechtmannSM, Joyner DC, Elias DA, Bailey KL, Hurt RA, Preheim SP, Sanders MC,Yang J, Mueller MA, Brooks S, Watson DB, Zhang P, He Z, Dubinsky EA,Adams PD, Arkin AP, Fields MW, Zhou J, Alm EJ, Hazen TC. 2015. Naturalbacterial communities serve as quantitative geochemical biosensors.mBio 6:e00326-15. https://doi.org/10.1128/mBio.00326-15.

40. Auckland Regional Council. 2010. State of the Auckland region. Auck-land Regional Council, Auckland, New Zealand.

41. Hewitt AE. 2010. New Zealand soil classification, 3rd ed. Landcare Re-search Science Series No. 1. Manaaki Whenua Press, Lincoln, New Zea-land.

42. IUSS Working Group. 2014. World reference base for soil resources: 2014international soil classification system for naming soils and creatinglegends for soil maps. FAO, Rome, Italy.

43. Hill RB, Sparling GP. 2009. Soil quality monitoring. Land and soilmonitoring: a guide for SoE and regional council reporting. Land Mon-itoring Forum, New Zealand.

44. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, GlöcknerFO. 2013. Evaluation of general 16S ribosomal RNA gene PCR primers forclassical and next-generation sequencing-based diversity studies. Nu-cleic Acids Res 41:e1. https://doi.org/10.1093/nar/gks808.

45. Edgar RC. 2010. Search and clustering orders of magnitude faster thanBLAST. Bioinformatics 26:2460 –2461. https://doi.org/10.1093/bioinformatics/btq461.

46. Edgar RC. 2013. UPARSE: highly accurate OTU sequences from microbialamplicon reads. Nat Methods 10:996 –998. https://doi.org/10.1038/nmeth.2604.

47. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A,Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenestaxonomy with explicit ranks for ecological and evolutionary analyses ofbacteria and archaea. ISME J 6:610 – 618. https://doi.org/10.1038/ismej.2011.139.

48. Clarke KR, Gorley RN. 2015. PRIMER v7: user manual/tutorial. PRIMER-E,Plymouth, Plymouth, UK.

Bacterial Indicators of Soil Health Applied and Environmental Microbiology

January 2017 Volume 83 Issue 1 e02826-16 aem.asm.org 13

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