MICROBIAL ECOLOGY

crossm Bacteria as Emerging Indicators of Soil Condition 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, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, Canterbury, New Zealandb; Department of Informatics 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 of

soil ecosystems and have great potential as novel indicators of environmental perturbations. To assess how they are affected by anthropogenic activity and to determine their ability to provide alternative metrics of environmental health, we sought to define which soil variables bacteria respond to across multiple soil types and land uses. We determined, through 16S rRNA gene amplicon sequencing, the composition of bacterial communities in soil samples from 110 natural or human-impacted sites, located up to 300 km apart. Overall, soil bacterial communities varied more in response to changing soil environments than in response to changes in climate or increasing geographic distance. We identified strong correlations between the relative abundances of members of Pirellulaceae and soil pH, members of Gaiellaceae and carbon-to-nitrogen ratios, members of Bradyrhizobium and the levels of Olsen P (a measure of plant available phosphorus), and members of Chitinophagaceae and aluminum concentrations. These relationships between specific soil attributes and individual soil taxa not only highlight ecological characteristics of these organisms but also demonstrate the ability of key bacterial taxonomic groups to reflect the impact of specific anthropogenic activities, even in comparisons of samples across large geographic areas and diverse soil types. Overall, we provide strong evidence that there is scope to use relative taxon abundances as biological indicators of soil condition.

Received 11 October 2016 Accepted 17 October 2016 Accepted manuscript posted online 28 October 2016 Citation Hermans SM, Buckley HL, Case BS, Curran-Cournane F, Taylor M, Lear G. 2017. Bacteria as emerging indicators of soil condition. Appl Environ Microbiol 83:e0282616. https://doi.org/10.1128/AEM.02826-16. Editor Frank E. Loeffler, University of Tennessee and Oak Ridge National Laboratory Copyright © 2016 American Society for Microbiology. All Rights Reserved. Address correspondence to Gavin Lear, [email protected].

IMPORTANCE The impact of land use change and management on soil microbial

community composition remains poorly understood. Therefore, we explored the relationship between a wide range of soil factors and soil bacterial community composition. We included variables related to anthropogenic activity and collected samples across a large spatial scale to interrogate the complex relationships between various bacterial community attributes and soil condition. We provide evidence of strong relationships between individual taxa and specific soil attributes even across large spatial scales and soil and land use types. Collectively, we were able to demonstrate the largely untapped potential of microorganisms to indicate the condition of soil and thereby influence the way that we monitor the effects of anthropogenic activity on soil ecosystems into the future. KEYWORDS biogeography, biological indicator, soil health, soil microbiology

S

oil 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 how this variation correlates with soil health. There is great promise for using bacterial January 2017 Volume 83 Issue 1 e02826-16

Applied and Environmental Microbiology

aem.asm.org 1

Hermans et al.

Applied and Environmental Microbiology

community composition or the relative abundances of individual taxa as indicators of the state of soil environments at the regional or even continental scale. Recent advances in next-generation sequencing technologies now make this a plausible and attractive avenue of research, leading to the proposal that bacterial community data are capable of providing alternative metrics of environmental health and production potential (9). If shown to be reliable, microbial community indicators could offer significant advantages over traditional chemical and biological measures in terms of the relative speed and ease of data analysis and of the minimization of site disturbance during sample collection (10). For bacterial community attributes to be a viable indicator of soil condition, it is desirable that natural spatial variations in bacterial community composition be less than the variation caused by anthropogenic factors. A consensus appears to have emerged that environmental factors, rather than dispersal limitations, are dominant drivers of bacterial community composition (5, 11). The reduced role of dispersal limitation in determining the beta-diversity of microbial communities, even across broad spatial scales, is presumed to be supported by a global dispersal of microbial cells, including those carried on major atmospheric currents (12) and oceanic currents (13). While there have been numerous bacterial biogeography studies that have employed DNA-fingerprinting techniques over large spatial scales (4, 5), DNAsequencing studies at similar scales are comparatively scarce. To date, most sequencing studies have analyzed relatively small numbers of samples, environments, and land uses or only small spatial scales where the effect of dispersal limitation can already be presumed 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 relationships observed between bacterial communities and soil environmental factors are pervasive or if they are instead strongly mediated by geographic location. This would be the first step toward supporting the broad-scale use of bacterial data as a viable indicator of soil health. In support of their ability to indicate the condition of the soil environment, previous biogeographic studies have identified several variables that correlate with changes in soil bacterial community composition. Most notable is the evidence that pH influences bacterial communities at the regional (14) and continental (4, 5) scales. Other variables such as the carbon-to-nitrogen ratio, moisture content, and soil temperature also correlate with changes in soil bacterial communities (5). However, relationships between bacterial communities and critical variables associated with the nature and intensity of human land use are frequently overlooked or are studied in isolation. These include concentrations of the many heavy metals that accumulate and impact biological 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 can correlate negatively with stock density and can ultimately impact the production potential of agricultural land (17, 18). The pairing of large-scale surveys of soil bacterial communities to data gathered through long-term soil monitoring programs (5, 19, 20) provides opportunities to uncover and quantify the strength of relationships between bacterial communities and a much wider range of soil physicochemical variables than had previously been achieved. This would then define which chemical and physical stresses on the soil environment are reliably portrayed by bacterial communities. To date, many studies have assessed changes in bacterial communities only as a whole rather than assessing the responses of individual taxa (4, 19). Others have restricted 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 community responses at lower taxonomic levels, such as the genus level, could highlight important trends that might not always be observed in the higher taxonomic ranks (24). The absence of studies assessing the responses of important taxa on an individual basis hinders not only our ability to expand our knowledge of the ecological attributes of January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 2

Bacterial Indicators of Soil Health

Applied and Environmental Microbiology

these important community members but also our ability to truly assess the potential of bacterial taxa to serve as biological indicators of ecosystem health. By pairing bacterial community data and extensive metadata gathered from 110 sites, we asked three main questions. First, is variation in bacterial communities more strongly related to environmental changes or to the geographic distance separating the communities? We predict that soil bacterial communities would be more strongly correlated with soil environmental factors rather than with purely spatial factors. This would suggest that bacterial community data may be suitable for the assessment of soil status across the geographic area from which samples were taken. Second, which environmental variables correlate with changes in bacterial community composition? While we expect to find, consistent with other studies, that pH has a dominant effect on bacterial communities, we also anticipate that we will be able to uncover important relationships between bacterial community structure and other soil variables that are indicators of soil condition. Third, can the abundances of different individual taxa be used to monitor soil condition? Determining how individual taxa respond to a range of environmental factors, and especially to changes in soil variables brought about by anthropogenic activity, may have important implications for how we monitor the health of our soils in the future. RESULTS After quality filtering, we obtained 5.46 million sequencing reads with an average length of 418 bp. After rarefying the data from each sample to 2,000 reads, we identified 17,495 unique operational taxonomic units (OTUs) in our data set. These OTUs represented 56 bacterial phyla, 613 families, and 987 genera. Relationships among spatial variables, soil factors, and bacterial community composition at the regional scale. Linear regression analysis revealed that, in general, the five different categories of land uses showed similar results in terms of the relationship between bacterial community dissimilarity and geographic distance, climatic dissimilarity, or dissimilarity in the soil environment (Fig. 1). Notably, the exotic forest sites and dry stock sites seemed to show slightly steeper trends in assessments of the increase in community dissimilarity with geographic distance, while the indigenous forest sites showed the steepest increase in community dissimilarity as the soil environment became more dissimilar. A significant relationship between bacterial community dissimilarity and changes in climate was observed only for dairy and dry stock sites, indicating that, overall, this was not an important variable affecting the bacterial communities at the scale of our sample collection. Similarly, for most of the land uses, the increase in bacterial dissimilarity with increasing geographic distance was minimal. These results are further confirmed by the spatial pattern observed across the study area, where communities of bacteria with similar compositions formed clusters in different parts of the study area (Fig. 2) rather than a simple, distance-based gradient of increasing dissimilarity being observed. Overall, the increase in dissimilarity as the soil environments became more dissimilar was steeper than for the other two variables measured. Consistent with this, for all combined samples, soil variables explained the greatest amount of variability in community composition (51% [see Fig. S3 in the supplemental material] compared to the 5% explained by a combination of spatial and climatic factors). This general trend was consistent for the compositions of phyla and classes 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 plant available phosphorus) accounted for a larger amount of variation in bacterial community composition than any of the other variables (Fig. 3). Specifically, distance-based multivariate multiple regression showed that pH accounted for the greatest amount of variation in community composition (9.6%) but particularly with respect to the abundances 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 January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 3

Hermans et al.

Applied and Environmental Microbiology

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 within each 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.

January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 4

Bacterial Indicators of Soil Health

Applied and Environmental Microbiology

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

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 of Proteobacteria, this was primarily driven by the relationship between C:N and the abundance of the Gammaproteobacteria (19.4%). Indeed, pH was still more important in explaining the abundance of the members of the classes Betaproteobacteria (15.2%) and Deltaproteobacteria (25.2%), while Olsen P was more important in explaining the abundance of the members of the class Alphaproteobacteria (28.9%). The majority of variation in the relative abundance of Bacteroidetes that could be accounted 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 soil variable in the form of Pearson’s correlation coefficient values. Most of the bacterial phyla showed a positive relationship with pH, increasing in abundance as soils became more neutral (Fig. 3). The exceptions to this are the phyla Alphaproteobacteria, Acidobacteria, and WPS-2, which all decreased in abundance as soils became less acidic. Proteobacteria, Alphaproteobacteria, Gammaproteobacteria, Acidobacteria, and WPS-2 all increased in abundance with increasing C:N, while Betaproteobacteria, Actinobacteria, Planctomycetes, and Firmicutes all decreased in abundance with increasing C:N. Alphaproteobacteria showed a negative correlation with the level of Olsen P in the soil, while Betaproteobacteria, Gammaproteobacteria, Chloroflexi, and Bacteroidetes were positively correlated with this variable. The relationship of Bacteroidetes with aluminum was negative. Conversely, Chloroflexi increased in abundance as concentrations of copper increased. January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 5

Hermans et al.

Applied and Environmental Microbiology

FIG 3 Relationship between bacterial community composition or relative taxon abundances and each soil variable. The radius of each circle represents the amount of variation in community composition or taxon abundance that was accounted for by each soil variable, based on adjusted R-squared values from distance-based multivariate multiple regression analyses; only statistically significant (P value ⬍ 0.05) contributions are shown, based on 999 permutations of the data. Additionally, the univariate relationship between the abundance of each taxon and soil variables, calculated using Pearson’s correlation coefficient, is represented by the color of the circle (blue represents a negative correlation; orange represents a positive correlation). Phyla or classes are ordered according to overall abundance in all the samples, from most abundant (top) to least abundant (bottom). Daggers (†) indicate that some soil variables were correlated with other variables in the data set, leading to some being removed from analysis, as detailed in Table S3. NH4-N was also included in the analysis, but the results did not reveal a significant relationship with bacterial community composition or relative taxon abundance and are therefore not shown.

Soil pH, C:N, and concentrations of Olsen P, aluminum, and copper were the only variables 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 attributes and the abundance of bacteria grouped to the genus level. Where possible, the genus which correlated most strongly with each variable, in terms of relative abundance, was selected for further analysis (Fig. 4). However, three of the four “genera” could not be identified beyond the family level and thus may represent groups of genera that simply remain to be differentiated taxonomically. Copper was ultimately excluded from the analyses as none of the groups of organisms that showed a strong relationship with this variable could be identified beyond the class level. Members of the family Pirellulaceae were strongly and positively correlated with soil pH, increasing in abundance in more neutral soils (r ⫽ 0.60, P ⬍ 0.001; Fig. 4a). There was 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 Alphaproteobacteria, 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 negatively correlated 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 indicators are to be used to inform on the condition of soils under managed land uses, then significant relationships between key taxa and soil attributes should remain, even when soils under native land use are excluded from analysis. Excluding the indigJanuary 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 6

Bacterial Indicators of Soil Health

Applied and Environmental Microbiology

FIG 4 Relationships between specific soil variables and the abundances of four selected taxa. The Pearson’s correlation coefficient (r) value for each relationship is indicated; all correlations were significant (P values ⬍ 0.001). Each point represents one 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 soil variables that were correlated with other soil variables in the data set, which were removed. Thus, for example, relationships between the relative abundances of Bradyrhizobium and cadmium similar to those determined for Olsen P may be expected, as detailed in Table S3.

enous forest soil samples to assess only the response of taxa to soil variables in the human-impacted samples, all correlations were still significant. In fact, the correlation between aluminum and Chitinophagaceae became stronger (see Table S5 in the supplemental material). Horticultural soils had significantly higher soil pH than most of the other land uses, with the exception of dairy-farming sites (Fig. S4a). Despite these significant differences in soil pH across land uses, members of the family Pirellulaceae were correlated with this soil measure, even if restricting our analysis to only data from within horticultural sites (r ⫽ 0.39, P ⫽ 0.025), dairy sites (r ⫽ 0.64, P ⬍ 0.001), or indigenous 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 the relationship between Gaiellaceae and this variable remained significant for horticulture 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 genus Bradyrhizobium proved to be particularly well correlated with the level of Olsen P in horticultural soils (r ⫽ ⫺0.54, P ⬍ 0.001), which had significantly higher levels of Olsen P than all the other land use categories except dairy-farming sites. The Chitinophagaceae group was correlated with the concentration of aluminum within the 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 from horticultural and dairy land uses, respectively). January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 7

Hermans et al.

Applied and Environmental Microbiology

DISCUSSION Our study, which combined comprehensive bacterial community data with detailed site metadata, was able to identify key relationships of bacterial communities, or individual taxa, with their environment. Specifically, we showed that rather than purely spatial factors, the data that correlated most closely with changes in the bacterial communities were the soil environment data. Importantly, we demonstrated strong trends between individual taxa and soil variables known to be strongly influenced by anthropogenic activity. These specific relationships are significant, not only because they reveal interesting ecological attributes of these taxa, such as their tolerance or sensitivity to certain environmental conditions, but also because they highlight their potential to indicate the condition of agricultural and pastoral soils. The results from this study show that while a small amount of the variation in community composition is explained purely by spatial factors, a greater portion is explained by environmental variables, consistent with previous work (4, 19, 25). As also reported by Griffiths and colleagues (5), we confirmed that the soil environment is more important in structuring bacterial communities than climatic variables. Overall, this implies that, at the spatial scale investigated here, factors such as dispersal limitation do not appear to be important for structuring bacterial communities. Furthermore, it also suggests that bacterial communities may respond in a somewhat predictable manner to environmental variation brought about by land use change and management, indicating that bacterial community data may indeed be a useful tool for assessing the status of the soil, at least within the area from which our samples were collected. Consistent with what has been previously reported, pH explained the greatest portion of variability in bacterial community composition in our study. The results highlighted that this trend not only occurs in a wide range of geographic settings but also remains consistent even when collecting samples across larger spatial scales than normally considered (14) or indeed when using a DNA sequencing approach instead of DNA-fingerprinting approaches, which have dominated investigations of microbial biogeography for many years (4, 5). Another previously reported trend that we observed for our samples was the relationship between bacterial communities and the ratio of carbon to nitrogen in the soil (5, 26, 27). However, as predicted, we were able to uncover relationships between the composition of bacterial communities and important soil variables less frequently included in investigations of soil microbial biogeography. While 14 of the 17 representative soil variables included in the analyses were able to explain a significant portion of the variability in community composition, the correlations with Olsen P were of particular interest. Olsen P is strongly linked to land use, as phosphorus is an important component of the fertilizer that is applied to soils used for both horticultural and pastoral purposes (28). Many studies investigating soil bacterial communities have not included any measure of phosphorous in their analyses (see, e.g., references 21, 27, and 29), but levels of Olsen P have been previously identified as influencing the composition of bacterial communities (5). The relatively large effect observed in the present study could 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 anthropogenic activity and lower values generally recorded for soils under native forests. Additionally, Olsen P, which is a measure of plant available phosphorus, was correlated with the concentration of total phosphorus in our data set and also with the concentration of cadmium (see Table S3 in the supplemental material). Therefore, the relationship that bacterial 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 the soil 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 chemoorganotrophs (30). Members of this family have been proposed to be associated with January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 8

Bacterial Indicators of Soil Health

Applied and Environmental Microbiology

plants (31), and while previous studies have found links between their abundance and soil variables such as calcium, magnesium, and cation exchange capacity (32), correlations to the ratio of soil carbon to nitrogen, such as were found here, have not been previously reported. This shows that an increased understanding of soil bacteria, at various taxonomic levels, can be obtained through large-scale studies that include a wide range of soil variables. The negative correlation between the levels of Olsen P and the abundance of Bradyrhizobium, members of which fix nitrogen both as a symbiont on legume roots and in a free-living state, is of particular interest (33, 34). This finding is significant because rising concentrations of Olsen P across the study sites have been highlighted as being of recent concern (20, 35). This genus has previously been identified as being of interest in indicating the effects of agricultural land use; its abundance has been shown to be lower in land used for agriculture, increasing over time after the land was retired from agriculture (34). However, while our indigenous forest soils had significantly lower levels of Olsen P and the more intensive land uses had higher levels, the observed correlation between Bradyrhizobium and Olsen P is not simply due to the presence or absence of anthropogenic activity. The trend remains significant even if considering only the human-impacted sites. The consistent and prevalent patterns presented here suggest that monitoring this genus has potential for use as a biologically relevant indicator for important soil variables such as Olsen P and the overall effects of land management. Another important relationship was that between heavy metals and the abundance of several key taxa. Although heavy metals are naturally present in soils, their concentrations are constantly being altered and are influenced by the use of fertilizers, pesticides, and wastewater irrigation, as well as through contamination from industrial areas or large residential areas (36). In the present study, data from heavy metals were able to explain a significant portion of variability in the abundances of all the analyzed phyla and classes, with between 1 and 3 different metals correlating with any given phylum or class. Specifically, the concentrations of aluminum were strongly related to the abundance of members within the family Chitinophagaceae. Aluminum has been previously shown to affect the diversity of bacteria in agricultural soils (37) and to be linked to changes in bacterial diversity in forest ecosystems (38). However, it is important that the concentrations of aluminum were correlated with the concentrations of several other heavy metals (Table S3). Therefore, it is possible that the members of 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 to distinguish these relationships. Regardless, members of the family Chitinophagaceae appear to be good candidates as indicators of environmental perturbations and to provide 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 indeed capable of reflecting the changes occurring in a soil environment due to anthropogenic activity. While, as we have shown here, this could be measured based on the direct abundances of taxa known to respond to specific soil variables, there are other methods worth exploring in future work. The use of machine learning tools such as a random forest classifier trained on bacterial data to indicate the health state of soil environments would be of particular interest. Methods such as this have previously been successful in creating an in situ environmental indicator that can classify sites either 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 be applied to detect subtler changes induced by land use management, which would likely result in weaker, and less-specific, selective forces acting on the bacterial communities. To conclude, our study showed that changes in the soil environment, largely brought about by anthropogenic activity, correlate more strongly with changes in bacterial community composition than with spatial factors. Notably, while confirming the relationship between bacterial community composition and soil pH and C:N, we January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 9

Hermans et al.

Applied and Environmental Microbiology

TABLE 1 Groups of representative explanatory variables used to explain sources of variation in bacterial community composition and the relative abundances of selected bacterial taxaa Explanatory group Space Climate Soil

Variables included NZTM Easting and NZTM Northing Annual rainfall,b solar radiation, January maximum temp, July minimum temp, elevationb 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. 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. bVariable

also uncovered important relationships between the bacterial communities and Olsen P. Furthermore, we showed that several phyla were more strongly influenced by variables such as Olsen P, or by the concentration of heavy metals such as aluminum and copper, than by pH. Pairing data obtained from soil monitoring programs with bacterial community data presents unique opportunities to uncover important relationships between individual phyla, classes, or even genera and soil variables influenced by anthropogenic activity. The confirmation of a strong relationship between specific taxa and anthropogenesis-related soil variables suggests that monitoring the presence of these taxa could serve as a biologically relevant indicator of the condition of our soils. MATERIALS AND METHODS Sample 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 or bare 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 New Zealand Soil Classification (41) and the World Reference Base for Soil Resources (42). The soil order classifications (and equivalents in the World Reference Base [WRB]) that were included were Granular Soils (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), and Recent Soils (Fluvisols and Arenosols) (n ⫽ 4). Sites are further categorized as being dominated by indigenous forest, exotic forest, dairy pasture, dry stock pasture, or horticulture (43) (see Table S1 in the supplemental material). For microbial analyses, we collected five soil cores at each site (0 to 10 cm in depth, 2.5 cm in diameter), after removing leaf litter and plant biomass, across a transect at 10-m intervals. These samples were kept on ice until they could be transferred to ⫺20°C storage until further use. We composited an additional 25 soil cores collected from the same transect at 2-m intervals for soil chemical analyses (Table 1), while we took intact soil cores (0 to 9 cm in depth, 10 cm in diameter) at 15-m intervals for soil physical analyses (Table 1) (43). Molecular methods. Before DNA extraction, we homogenized each thawed soil sample by manual mixing. We used PowerSoil-htp 96-well DNA isolation kits (Mo Bio Laboratories Inc., CA, USA) following the manufacturer’s instructions but with the following minor modifications: (i) mechanical lysis was performed by agitating the plates in a Qiagen TissueLyser II instrument (Retch) for 4 min at a frequency of 30 Hz; (ii) the ethanol air-drying time was extended to 15 min; and (iii) plates were incubated at room temperature 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, we amplified V3/V4 regions of bacterial 16S rRNA genes from each soil extract using modifications of the primers 341F (5=-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3=) and 785R (5=-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3=). This primer pair has been demonstrated to provide good coverage for bacteria and is purposely designed for optimal use on Illumina 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 well as 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 for 5 min. We individually purified PCR products using SequalPrep normalization plates (Invitrogen) or DNA Clean & Concentrator kits (Zymo Research), per the instructions of the manufacturers. Finally, we measured 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. The amplified material was then submitted to New Zealand Genomics Ltd. for sequencing on an Illumina MiSeq instrument using 2-by-300-bp chemistry. Prior to DNA sequencing, the sequencing provider January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 10

Bacterial Indicators of Soil Health

Applied and Environmental Microbiology

attached a unique combination of Nextera XT dual indices (Illumina Inc., USA) to the DNA from each sample to allow multiplex sequencing. Bioinformatic methods. The DNA sequence data were quality filtered, after which we picked de novo operational taxonomic units (OTUs) using USEARCH v 7.0 (45). Forward and reverse reads were merged using the fastq_mergepairs command. We truncated reads at the first position that had a quality score (Q score) of less than 3 and set the minimum length of the merged read to 200 bp. We then trimmed the first 20 bp from the start of all the merged sequences using the -fastq_filter command since this 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 clustered 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) before randomly 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 physicochemical 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 spatial analyst toolset were used to obtain climate and site aspect data (Table S2), based on the site location data (New Zealand Transverse Mercator [NZTM] Eastings and Northings). When a large range of soil and climatic variables are measured, several are usually correlated with each other, and this could have undesirable effects for downstream analyses. We therefore identified highly correlated explanatory variables (with a Pearson’s correlation value greater than 0.6 [either negative or positive]) and included only one of the representative variables in downstream analyses. This led us to keep 22 variables (we discarded 22 variables; Table S3). For all reported results, any significant correlation of bacterial community composition, or taxon abundance, with a representative explanatory variable could equally be 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 2,000 DNA sequence reads. To eliminate any biases associated with unequal levels of coverage across sites, we calculated centroid bacterial community data for each site by taking the mean abundance value for each OTU from three randomly selected samples on each transect. We then assessed differences in bacterial community composition by calculating the Bray-Curtis dissimilarities for each pair of samples, using the averaged OTU abundances for each site. We also assessed differences in community composition at the taxonomic levels of phylum, class, and genus. Bray-Curtis dissimilarity matrices were generated in R v3.2.1 using the ‘vegan’ package. We used nonmetric multidimensional scaling (nMDS) of the Bray-Curtis dissimilarity matrix from phylum 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 second nMDS axis scores to generate a geographical representation of the spatial patterns in community composition. We used distance decay analysis and linear regression to further investigate patterns in the bacterial communities for each of the five land uses separately. For this, pairwise comparisons of bacterial community dissimilarities within each land use category (Bray-Curtis measures based on OTU abundances) were plotted against geographic distance or dissimilarity in either climatic variables or the soil physicochemical attributes. Differences in climate and soil physicochemical attributes among samples were quantified by calculating the Euclidean distances among samples based on the first 10 principal components for climatic variables and the first 17 principal components for soil attributes (calculated from the climate and soil variable data in Table 1). Principal-component analysis could not be performed on the soil variables from the exotic forest samples, due to a shortage of sites, and therefore the changes in 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 dissimilarity and geographic distance, climate dissimilarity, or soil physicochemical attributes for each land use, where possible. The three different groups of explanatory variables (space, climate, and soil; Table 1) were also used to explain the variations in community composition and the abundances of specific phyla using variance partitioning procedures as previously described (11). To explain variations in community composition based on the relative abundances of OTUs, Bray-Curtis dissimilarity matrices were used as the input data for variance partitioning. Conversely, the abundances of specific phyla were used as univariate response variables, expressed as averaged abundance values at each site; the phyla selected for further investigation were those whose abundances had a Pearson’s correlation coefficient value greater than 0.50 with the first two nMDS axes in analyses of all samples (Fig. S1). Due to the large number of OTUs in the Proteobacteria, the four most abundant classes within this phylum were also selected for further investigation. To explore the relationship between the soil environment and both bacterial community composition and the abundance of individual taxa, we used distance-based multivariate multiple regression of the Bray-Curtis distance matrices. Models were built in Primer v.7 by applying a forward selection procedure for the soil variables using adjusted R2 values as a selection criterion. Statistical significance was assessed by 999 permutations, and only significant variables (P value ⬍ 0.05) were included in each model. Further, we determined the direction and strength of the univariate relationship between individual soil variables and phylum or class abundance using Pearson’s correlation coefficient. For phyla or classes where one specific soil variable was able to explain ⬎15% of the variation in abundance, January 2017 Volume 83 Issue 1 e02826-16

aem.asm.org 11

Hermans et al.

Applied and Environmental Microbiology

Pearson’s correlation coefficient values were calculated to determine which genera within these phyla showed strong relationships with the edaphic variables. To determine if correlations between the individual taxa and soil variables could be solely due to differences in abundance in native versus human-impacted sites, we repeated our analyses on subsamples containing data only from humanimpacted sites or only from single, anthropogenic land use types. Accession number(s). We deposited all amplicon sequence data associated with this article in the NCBI 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. ACKNOWLEDGMENTS We 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.

REFERENCES 1. Ranjard L, Poly F, Nazaret S. 2000. Monitoring complex bacterial communities using culture-independent molecular techniques: application to 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 plant growth-promoting bacteria for biocontrol of plant diseases: principles, mechanisms of action, and future prospects. Appl Environ Microbiol 71:4951– 4959. https://doi.org/10.1128/AEM.71.9.4951-4959.2005. 3. Wu T, Chellemi DO, Graham JH, Martin KJ, Rosskopf EN. 2008. Comparison of soil bacterial communities under diverse agricultural land management 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 soil bacterial 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. The bacterial 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 of soil microbial communities across nitrogen gradients. ISME J 6:1007–1017. 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 and function 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 ecological health. 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 maintenance and monitoring of freshwater health, p 129 –151. In Microbial biofilms: current research and applications. Horizon Scientific Press, Norfolk, UK. 11. Lear G, Washington V, Neale M, Case B, Buckley H, Lewis G. 2013. The biogeography 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, Anderson BE, Beyersdorf AJ, Ziemba LD, Bergin M, Nenes A, Konstantinidis KT. 2013. Microbiome of the upper troposphere: Species composition and prevalence, 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, Berry D, Jørgensen BB, Loy A. 2014. Endospores of thermophilic bacteria as tracers of microbial dispersal by ocean currents. ISME J 8:1153–1165. https://doi.org/10.1038/ismej.2013.225. January 2017 Volume 83 Issue 1 e02826-16

14. Lauber CL, Strickland MS, Bradford MA, Fierer N. 2008. The influence of soil properties on the structure of bacterial and fungal communities across 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 contaminants of urban runoff (Zn, Cu & Pb) accumulate in freshwater biofilm and modify 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 in agricultural soils of the European Union with implications for food safety. 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, Helmisaari H-S, Uri V, Zobel M. 2012. Bacterial community structure and its relationship to soil physico-chemical characteristics in alder stands with different 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, Guillemin 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 bacterial density 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, Saby NPA, Lelievre M, Maron PA, Morin FE, Bispo A, Jolivet C, Arrouays D, Lemanceau P. 2013. Turnover of soil bacterial diversity driven by widescale 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’s largest city after 15 years. Int J Environ Ecol Geol Geophys Eng 9:227–234. 21. Lauber CL, Hamady M, Knight R, Fierer N. 2009. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure 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 patterns of plants and animals. Ecology 92:797– 804. 23. Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N, Gilbert JA. 2014. Conditionally rare taxa disproportionately contribute to temporal 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 of Bt- 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 land use alter the structure of bacterial communities in Western Amazon soils. 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 bioaem.asm.org 12

Bacterial Indicators of Soil Health

27.

28. 29.

30.

31.

32.

33.

34.

35.

36.

37.

geography of ammonia-oxidizing bacterial communities in soil. Microb Ecol 58:435– 445. https://doi.org/10.1007/s00248-009-9517-9. Kuramae EE, Yergeau E, Wong LC, Pijl AS, van Veen JA, Kowalchuk GA. 2012. Soil characteristics more strongly influence soil bacterial communities than land-use type. FEMS Microbiol Ecol 79:12–24. https://doi.org/ 10.1111/j.1574-6941.2011.01192.x. During C. 1984. Fertilisers and soils in New Zealand farming. Sigma Print Limited, Petone, New Zealand. Lauber CL, Ramirez KS, Aanderud Z, Lennon J, Fierer N. 2013. Temporal variability in soil microbial communities across land-use types. ISME J 7:1641–1650. https://doi.org/10.1038/ismej.2013.50. Albuquerque L, da Costa MS. 2014. The family Gaiellaceae, p 357–360. In Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (ed), The prokaryotes. Springer, Berlin, Germany. 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 community. Agric Ecosyst Environ 213:86 –93. https://doi.org/10.1016/ j.agee.2015.07.025. Barbose Lima A, de Souza Cannavan F, Gomes Germano M, DiniAndreote F, Monteiro de Paula A, Cezar Franchini J, Geraldes Teixeira W, Tsai SM. 2015. Effects of vegetation and seasonality on bacterial communities in Amazonian dark earth and adjacent soils. Afr J Microbiol Res 9:2119 –2134. https://doi.org/10.5897/AJMR2015.7453. Pagan JD, Child JJ, Scowcroft WR, Gibson AH. 1975. Nitrogen fixation by Rhizobium cultured on a defined medium. Nature 256:406 – 407. https:// doi.org/10.1038/256406a0. 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 related to agricultural practices in long-term field experiments. Front Microbiol 4:104. https://doi.org/10.3389/fmicb.2013.00104. Taylor MD, Kim ND, Hill RB, Chapman R. 2010. A review of soil quality indicators and five key issues after 12 yr soil quality monitoring in the Waikato region. Soil Use Manag 26:212–224. https://doi.org/10.1111/ j.1475-2743.2010.00276.x. Wuana RA, Okieimen FE. 2011. Heavy metals in contaminated soils: a review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol 2011:e402647. https://doi.org/10.5402/2011/ 402647. Da Mota FF, Gomes EA, Marriel IE, Paiva E, Seldin L. 2008. Bacterial and

January 2017 Volume 83 Issue 1 e02826-16

Applied and Environmental Microbiology

38.

39.

40. 41.

42.

43.

44.

45.

46.

47.

48.

fungal communities in bulk soil and rhizospheres of aluminum-tolerant and aluminum-sensitive maize (Zea mays L.) lines cultivated in unlimed and limed cerrado soil. J Microbiol Biotechnol 18:805– 814. Faoro H, Alves AC, Souza EM, Rigo LU, Cruz LM, Al-Janabi SM, Monteiro RA, Baura VA, Pedrosa FO. 2010. Influence of soil characteristics on the diversity of bacteria in the southern Brazilian Atlantic forest. Appl Environ Microbiol 76:4744 – 4749. https://doi.org/10.1128/AEM.03025-09. Smith MB, Rocha AM, Smillie CS, Olesen SW, Paradis C, Wu L, Campbell JH, Fortney JL, Mehlhorn TL, Lowe KA, Earles JE, Phillips J, Techtmann SM, 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. Natural bacterial communities serve as quantitative geochemical biosensors. mBio 6:e00326-15. https://doi.org/10.1128/mBio.00326-15. Auckland Regional Council. 2010. State of the Auckland region. Auckland Regional Council, Auckland, New Zealand. Hewitt AE. 2010. New Zealand soil classification, 3rd ed. Landcare Research Science Series No. 1. Manaaki Whenua Press, Lincoln, New Zealand. IUSS Working Group. 2014. World reference base for soil resources: 2014 international soil classification system for naming soils and creating legends for soil maps. FAO, Rome, Italy. Hill RB, Sparling GP. 2009. Soil quality monitoring. Land and soil monitoring: a guide for SoE and regional council reporting. Land Monitoring Forum, New Zealand. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glöckner FO. 2013. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41:e1. https://doi.org/10.1093/nar/gks808. Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460 –2461. https://doi.org/10.1093/ bioinformatics/btq461. Edgar RC. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10:996 –998. https://doi.org/10.1038/ nmeth.2604. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610 – 618. https://doi.org/10.1038/ ismej.2011.139. Clarke KR, Gorley RN. 2015. PRIMER v7: user manual/tutorial. PRIMER-E, Plymouth, Plymouth, UK.

aem.asm.org 13