Arbuscular mycorrhizal fungi associated with a single agronomic plant host across the landscape: Community differentiation along a soil textural gradient

Soil Biology & Biochemistry 64 (2013) 191e199

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Arbuscular mycorrhizal fungi associated with a single agronomic plant host across the landscape: Community differentiation along a soil textural gradient Daniel J. Moebius-Clune a, Bianca N. Moebius-Clune b, Harold M. van Es b, Teresa E. Pawlowska a, * a b

Department of Plant Pathology & PlanteMicrobe Biology, Cornell University, 334 Plant Sciences Building, Ithaca, NY 14853-5904, USA Department of Crop & Soil Sciences, Cornell University, Ithaca, NY 14853-1901, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 June 2012 Received in revised form 14 December 2012 Accepted 24 December 2012 Available online 21 January 2013

The arbuscular mycorrhizal (AM) fungi (phylum Glomeromycota) are important for the functioning of terrestrial ecosystems because of their influence on plant nutrient relations and plant responses to stress. To assess the impact of dispersal limitation and identify niche-related environmental gradients affecting AM fungal community composition and structure, we studied AM fungal communities in an assemblage of maize fields across an eastern New York State landscape. We expressed AM fungal community differences in terms of abundance structure (BrayeCurtis dissimilarities), the presence of unique phylogenetic lineages (UniFrac), and mean phylogenetic relatedness between samples (mean patristic distance, MPD). We did not find strong evidence of dispersal limitation or isolation by distance within or between field sites. To identify environmental factors that may be related to community differentiation, we projected vectors of edaphic variables onto nonmetric multidimensional scaling (NMDS) ordinations of community dissimilarity measures. Of these factors, soil textural components appeared most strongly related to AM fungal community differences. We speculate that this pattern may be explained by the relationship between texture and soil moisture availability. In addition to soil textural components, phylogenetic measures of community differentiation suggested that AM fungal community structure was affected by nutrient concentrations, particularly Mg. Of the two phylogenetic indices of community differentiation, MPD was more consistent and stable with our data, whereas the UniFrac metric failed to be interpretable in several cases. Overall, our data suggest that, rather than phosphorus or pH, soil texture may have an influence on AM fungal community structure over large agroecological scales. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Arbuscular mycorrhizal fungi Available water capacity BrayeCurtis Dispersal limitation Glomeromycota Mean patristic distance Soil texture UniFrac

1. Introduction Many of the key ecosystem functions provided by the arbuscular mycorrhizal (AM) fungi (phylum Glomeromycota) such as improved host plant nutrient relations (Smith and Read, 2008), improved plant pathogen resistance (Linderman, 2000), and contributions to soil structure (Tisdall, 1991) differ between AM fungal species, and, as such, will vary with the community composition of AM fungi. Realized functional differences between AM fungal communities in facilitation of plant growth, nutrient acquisition, and reproductive success have been documented in both natural (Ji et al., 2010) and managed systems (Johnson, 1993). Thus understanding of what factors influence AM fungal community composition and how these communities are distributed across large * Corresponding author. Tel.: þ1 607 254 8726; fax: þ1 607 255 4471. E-mail address: [email protected] (T.E. Pawlowska). 0038-0717/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.soilbio.2012.12.014

spatial scales can contribute to our knowledge of terrestrial ecosystem functioning. AM fungal communities in natural ecosystems appear to be shaped by both niche-related factors and neutral processes (Dumbrell et al., 2010). AM fungal niche space is complex. As might be expected for obligate symbionts, the most noticeable nicherelated influences are those of host plant community composition and diversity (Bever et al., 1996; Burrows and Pfleger, 2002). A primary mechanism for this influence is a negative feedback between host plants and their fungal symbionts’ reproductive success (Bever, 2003). Other than host effects, mineral nutrient concentrations in soil seem to be important. The effects of P concentration on mycorrhization has been noted in several studies (Smith and Read, 2008), and it appears that in general, higher soluble P content in soil is associated with lower mycorrhizal root colonization rates and lower AM fungal diversity (Douds et al., 1993; Douds and Schenck, 1990). AM fungal communities also

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shift along environmental N deposition gradients (EgertonWarburton and Allen, 2000), and N fertilization of soil leads to changes in AM fungal community with potentially broad functional significance (Egerton-Warburton et al., 2007). Finally, fungal communities respond to soil pH and moisture gradients (Porter et al., 1987), and to soil type (Oehl et al., 2010). Interactions between these host-related and edaphic factors influence the realized niche of the AM fungi. In addition to the niche-related influences, recent work has highlighted the contribution of neutral processes, such as dispersal limitation, to AM fungal community composition and structure (Dumbrell et al., 2010; Lekberg et al., 2007). If species have similar traits related to their performance in the environment and to competitive ability, their distribution and thus community composition will be a product of stochastic processes governing arrival and local survival or extinction (Hubbell, 2001). According to the neutral theory, dispersal limitation is expected to result in greater compositional difference with increasing geographic distance, which is distinct from, but not mutually exclusive with, the patterns of spatial distribution controlled by biotic and abiotic factors. Exploration of fungal communities in managed agroecosystems offers the opportunity to separate the effects of host plant community from fungal dispersal and the influence of soil characteristics. In an earlier paper describing a landscape-level study of agricultural fields with a common host plant species (maize, Zea mays) distributed across eastern New York State, we characterized an assemblage of AM fungal communities that exhibit substantial species level dissimilarities in taxonomic composition, and differences in dominance (Moebius-Clune et al., 2013). Specifically, multiple fields were dominated by Claroideoglomus etunicatum with richness ranging from 3 to 11 taxa and differences in the identity of subdominant taxa. Other fields had little or no C. etunicatum and were dominated variously by species from the genera Paraglomus and Rhizophagus. Dispersal limitation could be a simple explanation for the community differentiation observed between these agricultural fields. However, even fields in close proximity differed markedly in composition, which is not expected for communities shaped primarily by dispersal limitation. In addition, we found that the landscape assemblage, as well as the communities within individual fields, showed a lognormal species abundance distribution. This pattern suggested that multiple niche-related mechanisms influence the AM fungal community composition and structure in our study system. The goal of the present study was to assess the impact of dispersal limitation, and elucidate the nature of the niche-related environmental factors that influence the composition and structure of AM fungal communities associated with maize in several fields across eastern New York State. We tested the hypothesis that AM fungal communities are shaped by dispersal limitation. This hypothesis would be supported by significant positive correlation between community dissimilarities and spatial distance between samples. To understand the impact of niche-related factors on AM fungal communities, we conducted indirect gradient analysis by fitting edaphic factor vectors into nonmetric multidimensional scaling (NMDS) ordinations of community dissimilarities. We further tested the significance of the relationships between key gradients in various soil characteristics identified by indirect gradient analysis, and community structural differences. In all our analyses, we expressed community differences in terms of abundance structure (BrayeCurtis dissimilarities), the presence of unique phylogenetic lineages (UniFrac), and mean phylogenetic relatedness between samples (mean patristic distance, MPD), and we compared performance of these metrics in terms of stability, interpretability in ordinations, and sensitivity to outliers.

2. Materials & methods 2.1. Sampling and identification of AM fungi AM fungal community sampling is described in detail in Moebius-Clune et al. (2013). In brief, we sampled soil immediately surrounding roots of maize plants from eight active conventionally managed fields in eastern New York State in a spatially explicit manner. The fields were distributed with a range of distances from 200 m to 400 km. At field A, which was laid out physically in four blocks, 20 samples were taken, at regularly spaced intervals along a transect in each of the blocks. Fields B through H were sampled using a modification of the methods used by the Cornell Soil Health Team (Gugino et al., 2009; Moebius et al., 2007), keeping individual samples from within a field separate rather than compositing them. Ten samples from each field were taken in a “relaxed W” arrangement across the field, allowing for a series of distances from 2 m to 60 m to be represented between samples in a field, with each distance represented multiple times. Greenhouse trap cultures with maize as a host were established for each individual sampling point. After spore extraction from each trap culture, ten random spores (fungal individuals) were selected from each sampling point and genotyped by sequencing PCRamplified fragments of the 50 -end of the large subunit (LSU) rRNA gene (GenBank accession numbers JN937121eJN937574). AM fungal operational taxonomic units (OTUs) were defined based on a 95% sequence similarity level. Taxonomic affiliation of OTUs was assessed by reconstructing their phylogenetic histories relative to named reference taxa. 2.2. Soil properties Soil data were obtained from the Cornell Soil Health Project (http://soilhealth.cals.cornell.edu/). These data include aggregate stability (AgSt), available water capacity (AWC), textural components (% sand, silt, clay), penetration resistance (PenRes), pH, extractable nutrients (P, K, Mg, Fe, Mn, Zn), organic matter (OM), active (permanganate oxidizable) carbon (ActC), potentially mineralizable N (PMN), and root health (RootH). AgSt was measured by rainfall simulation (Moebius et al., 2007). AWC was determined using pressure plates at field capacity and permanent wilting point equivalent pressures (Topp et al., 1993). PenRes (0e 15 cm depth) was quantified using a compaction tester (DickeyJohn, Auburn, WI); PenRes could not be measured accurately in Field H due to rockiness. The pH of each sample was measured in a 1:1 slurry with water, using a standard pH meter (Eckert and Sims, 1995). Plant available nutrients were extracted with Morgan’s solution (Morgan, 1941) and measured on an ICP spectrometer (Jobin Yvon, Kyoto, Japan), except for PO4eP, which was measured using an automated rapid flow analyzer (RFA/2, Alpkem), at the Cornell Nutrient Analysis Laboratory in Ithaca, NY. OM content was assessed by loss on ignition (Nelson and Sommers, 1996), and ActC by permanganate oxidation (Weil et al., 2003). PMN was measured by 7-day anaerobic incubation (Drinkwater et al., 1996). RootH values were determined visually using a Phaseolus vulgaris root pathogen pressure assay as described in Gugino et al. (2009). As organisms do not generally respond to edaphic factors linearly, Cornell Soil Health Test Report (CSHTR) scores were assigned to each measured soil property using nonlinear scoring functions, as described previously (Gugino et al., 2009; Idowu et al., 2009; Moebius et al., 2007; Moebius-Clune, 2010). The CSHTR scores interpret each soil property’s measured value with respect to anticipated constraints for crop growth and environmental impact on a scale of 0 (very constrained) to 100 (optimal).

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2.3. Community dissimilarity measures Between-sample and between-field community dissimilarity matrices were calculated using three dissimilarity measures, Braye Curtis (Bray and Curtis, 1957), UniFrac (Lozupone and Knight, 2005), and the mean pairwise patristic distance, MPD (Webb et al., 2008a). The BrayeCurtis dissimilarity measure (Bray and Curtis, 1957) is a commonly used taxon-abundance based statistic expressing the fraction of the total abundance between two samples that represents difference between them. This index meets a number of important criteria for ecological dissimilarity measures (Clarke et al., 2006), and performs well in comparison with other ecological dissimilarity measures in simulation studies (Bloom, 1981; Faith et al., 1987). BrayeCurtis was calculated from the sample by OTU matrix as BCjk ¼ Sijxij  xikj/Si(xij þ xik), where BCjk is the BrayeCurtis dissimilarity between samples j and k, xij is the abundance of taxon i in sample j, and xik is the abundance of taxon i in sample k. UniFrac is a phylogeny-based statistic representing the fraction of the total pooled phylogenetic tree branch length that is not shared between samples (Lozupone and Knight, 2005). It was calculated as the ratio of the branch length within the tree that was subtended by members of only one of the samples being compared, to the total of the branch length subtended by members of either sample. MPD is also a phylogeny-based statistic calculated as the mean pairwise patristic distance between all members of two samples (Webb et al., 2008a). To compute UniFrac and MPD, maximum likelihood phylogenetic trees of AM fungal LSU rRNA gene sequences were reconstructed with RAxML (Stamatakis, 2006; Stamatakis et al., 2008) using the general time reversible (GTR) nucleotide substitution model (Tavaré, 1986) with gamma (G) rate variation. UniFrac distances were calculated using the Fast UniFrac (Hamady et al., 2010) online portal (http://bmf.colorado. edu/fastunifrac/). MPD was computed using the comdist function in Phylocom v4.1 (Webb et al., 2008a). To assess the influence of very low abundance taxa, additional dissimilarity matrices were calculated for reduced sets of taxa. For the first reduced set, singleton and doubleton taxa were removed. For the second reduced set, in addition to singletons and doubletons, taxa appearing in only one (uniques) or two samples (duplicates) within the set were removed. For the third reduced set, only the most abundant 25% of taxa (9 OTUs) were retained. To calculate BrayeCurtis dissimilarities, taxa were eliminated from the sample by OTU matrix. To calculate UniFrac and MPD on reduced taxon sets, ML phylogenetic trees were recalculated from rRNA gene alignments after removing sequences representing eliminated OTUs. We interpreted primarily the results from the full sample set, and examined the reduced sets to assess the empirical performance of the different metrics with our data set. The significance of between-field BrayeCurtis, UniFrac, and MPD differences was assessed by permutational MANOVA (Anderson, 2001) using the function adonis in the vegan library (Oksanen et al., 2011) in R (R Development Core Team, 2010). To measure the relative contributions of taxa to between-sample and between-field differences, we used similarity percentage (SIMPER) analysis (Clarke, 1993) implemented in PAST v2.04 (Hammer et al., 2001).

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following Legendre (2000) to carry out a standard Mantel test on distance and dissimilarity matrices comprising all fields together, and permuting location only within field. We further tested for dispersal limitation at the within-field level in each field individually using permutational exact Mantel tests implemented in zt (Bonnet and Van de Peer, 2002), which we also used for dispersal limitation testing for between-field patterns a the landscape level. Dispersal limitation within field A was assessed separately, as this field was spatially arranged in four blocks, by assessing spatial structuring of dissimilarity by the significance of block in a permutational MANOVA. 2.5. Indirect gradient analysis Nonmetric multidimensional scaling (NMDS) ordinations (Kruskal, 1964) for each dissimilarity matrix, including BrayeCurtis, UniFrac, and MPD, were calculated using PC-Ord v.6 (MjM Software Design, Gleneden Beach, OR), on three axes, using 50 runs with random starting configurations. NMDS is particularly well suited for ordination of ecological community data (McCune et al., 2002). It can operate with any meaningful measure of community dissimilarity, and avoids problems encountered in eigenvector-based ordination of some dissimilarity indices that are widely used in ecological research, such as semi-metrics like the BrayeCurtis measure (Legendre and Legendre, 1998). By fitting samples in ordination based on ranks, NMDS allows the identification of environmental gradients to which the focal organisms respond in a nonlinear manner (Clarke, 1993). Ordinations for each dissimilarity measure reached acceptable (Clarke, 1993) stress and stability levels (stress < 0.1, instability < 0.00001). It was necessary to remove two outlying samples from the BrayeCurtis ordination, and three from the UniFrac ordination. Outliers were identified as points lying outside of the interquartile range (IQR) of distances from the origin by more than 100 times the width of that IQR. This procedure identifies only extreme multivariate outliers, which otherwise render the ordinations uninterpretable. We assessed the relationship between the variation in community composition as expressed by the calculated NMDS, and edaphic variables, including soil properties and calculated CSHTR scores, by vector fitting, or projecting vectors of these variables into NMDS ordination space. The strength of the association with each edaphic variable was assessed by the r2 for the regression-based fit of its vector to each calculated NMDS. Significance was assessed by distribution-free P value obtained through 10,000 random permutations. Analysis was carried out using the vegan package in R (Oksanen et al., 2011; R Development Core Team, 2010). To further characterize the relationships between the environmental gradients suggested by vector fitting and the differentiation between AM fungal communities, we examined the univariate relationships between key environmental variables and field-level community characteristics measured previously (Moebius-Clune et al., 2013), including observed taxon richness S, Shannon diversity eH (MacArthur, 1965), Heip’s evenness E (Heip, 1974), Simpson’s dominance l (Simpson, 1949), phylogenetic diversity PD (Faith, 1992), and net relatedness index NRI (Webb, 2000) (Table S1). Spearman correlations were computed in SAS v9.3 (SAS Institute, Cary, NC).

2.4. Dispersal limitation 3. Results We tested the dispersal limitation hypothesis that community dissimilarity increases with distance using Mantel tests. Probability values P  0.05 were considered significant, and probability values 0.05 < P  0.1 were considered marginally significant and noteworthy. To test for dispersal limitation at the within-field level treating all fields together, we used a custom function written in C,

3.1. Community dissimilarities All three dissimilarity measures (BrayeCurtis, UniFrac, and MPD) indicated that there were significant differences between AM fungal communities in maize fields distributed across the

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landscape in eastern New York State (P < 0.0001 in each, Table 1). Field identity accounted for 23e41% of the variation in inter-sample dissimilarity (BrayeCurtis r2 ¼ 0.414, UniFrac r2 ¼ 0.256, MPD r2 ¼ 0.228). The contribution of individual taxa to dissimilarities between samples and to dissimilarities between fields reflected the overall abundance of these taxa (between samples r2 ¼ 0.996; between fields r2 ¼ 0.989; Table 2).

We tested the significance of patterns indicative of dispersal limitation at: (i) the within-field level for data from all fields collectively, (ii) the within-field level examining each field separately, and (iii) the between-field level across the landscape. (i) In simultaneous testing of the within-field relationship, across all fields sampled with the same spatial scheme (Fields BeH, Table 3), we found insufficient evidence to demonstrate a significant positive linear relationship between community dissimilarity and physical distance for each of the dissimilarity measures, BrayeCurtis, UniFrac, and MPD. (ii) Furthermore, none of the individual fields, examined separately, revealed a positive relationship between distance and community dissimilarity. Spatial structuring of dissimilarities in the blocked field (Field A) was non-significant as well. (iii) At the landscape level, we found a small and marginally significant (P ¼ 0.09) increase in between-field dissimilarity with increasing physical distance for the BrayeCurtis measure, which remained after removal of low-abundance taxa (Table 4). We found a marginally significant relationship between UniFrac dissimilarity and physical distance between fields (P ¼ 0.07), which was not apparent when the 4.8% of individuals appearing only as singletons or doubletons were excluded. Finally, we found no significant relationship between MPD dissimilarities and distance between fields. 3.3. Indirect gradient analysis 3.3.1. Identification of influential edaphic factors Soil nutrient values in New York State maize fields ranged in variation, with coefficient of variation, CV, between 0.42 (K) and 0.88 (P), while other characteristics exhibited a greater range in variation, from a CV of 0.17 (root health) to 1.31 (clay) (Table 5). We projected each of these variables independently onto the NMDS ordinations of each of the dissimilarity measures to identify hypothetical gradients in edaphic factors likely related to the differentiation between samples in composition (Table 6). The strongest gradients identified in the BrayeCurtis ordination were clay (r2 ¼ 0.60) and sand (r2 ¼ 0.57) (Fig. 1, Table 6). Gradients were weaker in the phylogeny-based ordinations. In the UniFrac ordination, the nonlinear available water capacity (AWC) score was most strongly related to sample position in the ordination Table 1 Permutational MANOVA of AM fungal community inter-sample dissimilarity among New York State maize fields. BrayeCurtis, UniFrac, and mean patristic distance, MPD, were modeled in response to field identity. Diversity measure

Source of variation

df

Sums of squares

Mean squares

F

r2

P

Braye Curtis

Field Residuals Total Field Residuals Total Field Residuals Total

7 75 82 7 75 82 7 75 82

13.197 18.672 31.869 2.3485 6.8294 9.1779 2.8487 9.6371 12.4858

1.88533 0.24896

7.5728

<0.0001

0.33549 0.09106

3.6844

0.40696 0.12849

3.1671

0.41411 0.58589 1 0.25588 0.74412 1 0.22816 0.77184 1

MPD

OTU

OTU31 OTU33

3.2. Dispersal limitation

UniFrac

Table 2 Relative contributions of AM fungal OTUs to between-sample and between-field dissimilarities identified by similarity percentage (SIMPER) analysis.

<0.0001

<0.0001

OTU17 OTU25 OTU30 OTU32 OTU3 OTU9 OTU21 OTU26 OTU7 OTU11 OTU14 OTU27 OTU16 OTU19 OTU20 OTU1 OTU4 OTU5 OTU8 OTU12 OTU2 OTU6 OTU10 OTU13 OTU15 OTU18 OTU22 OTU23 OTU24 OTU28 OTU29

Taxonomic affiliation

Claroideoglomus etunicatum C. etunicatum/claroideum/ luteum lineage Funneliformis mosseae Paraglomus occultum C. etunicatum/claroideum/ luteum lineage Glomus trimurales Rhizophagus diaphanus Rhizophagus sp. Archaeospora schenckii Ambispora sp. R. irregulare Rhizophagus cf. clarus G. viscosum Glomeromycota Funneliformis sp. Paraglomeraceae P. brasilianum Diversisporaceae R. clarus Rhizophagus sp. R. irregulare Rhizophagus cf. clarus R. diaphanus R. irregulare Rhizophagus cf. clarus Rhizophagus cf. clarus G. viscosum Glomeromycota Ar. schenckii P. occultum P. occultum G. microaggregatum G. candidum

Relative abundance

Relative dissimilarity contribution Between samples

Between fields

0.356 0.130

0.310 0.124

0.299 0.130

0.078 0.071 0.062

0.078 0.075 0.059

0.075 0.058 0.076

0.055 0.048 0.037 0.027 0.021 0.016 0.011 0.011 0.009 0.007 0.007 0.007 0.005 0.005 0.005 0.005 0.005 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002

0.060 0.050 0.041 0.033 0.021 0.019 0.017 0.014 0.010 0.007 0.008 0.007 0.006 0.007 0.005 0.005 0.007 0.004 0.003 0.003 0.003 0.003 0.003 0.002 0.005 0.003 0.004 0.003

0.056 0.053 0.044 0.035 0.028 0.018 0.015 0.013 0.011 0.008 0.009 0.008 0.006 0.006 0.005 0.006 0.006 0.004 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.004 0.004

(r2 ¼ 0.42), followed by linear gradients in silt (r2 ¼ 0.39), Zn (r2 ¼ 0.39), and Mg (r2 ¼ 0.38). However, in the MPD ordination AWC score ranked third (r2 ¼ 0.27) after Mg (r2 ¼ 0.28) and clay (r2 ¼ 0.27) (Fig. 1, Table 6). 3.3.2. Ordination outliers, and rare and infrequent taxa To address the influence of rare and infrequent taxa, and to further explore the sensitivity in ordination of the different dissimilarity measures to extreme multivariate outlier samples, we recalculated ordinations with reduced data sets, eliminating progressively greater numbers of taxa (Table 4). Removing singletons

Table 3 Dispersal limitation tests for AM fungal communities within New York State maize fields. Mantel rM statistics and P values (in parentheses) for within-field tests of increasing BrayeCurtis, UniFrac, and mean patristic distance, MPD, dissimilarity with increasing distance; P values have not been adjusted for multiple testing. Field

B C D E F G H All

Method BrayeCurtis

UniFrac

0.06 0.10 0.20 0.15 0.09 0.37 0.25 0.007

0.05 0.03 0.07 0.09 0.04 0.39 0.16 0.043

(0.367) (0.244) (0.889) (0.755) (0.255) (0.883) (0.195) (0.529)

MPD (0.508) (0.409) (0.634) (0.288) (0.407) (0.905) (0.621) (0.773)

0.01 0.09 0.07 0.03 0.18 0.32 0.17 0.002

(0.408) (0.694) (0.646) (0.546) (0.143) (0.848) (0.706) (0.499)

D.J. Moebius-Clune et al. / Soil Biology & Biochemistry 64 (2013) 191e199 Table 4 Dispersal limitation tests for AM fungal communities between New York State maize fields. Mantel rM statistic and P values (in parentheses) for the relationship between BrayeCurtis, UniFrac, and mean patristic distance, MPD, dissimilarity measures and geographic distance between fields, including the effects of data denoising by removal of low abundance taxa. 1st reduction removed singletons and doubletons, i.e. 16 of 32 OTUs, representing 4.8% of total abundance; 2nd reduction removed singletons, doubletons, uniques and duplicates, i.e. 19 of 32 OTUs, representing 7.3% of total abundance; 3rd reduction retained only the top 9 taxa, removing 13.7% of total abundance. Data included

Method BrayeCurtis

UniFrac

MPD

Full data set 1st reduction 2nd reduction 3rd reduction

0.29 0.30 0.30 0.29

0.56 0.23 0.23 0.01

0.34 0.32 0.37 0.39

(0.0874) (0.0821) (0.0819) (0.0816)

(0.0739) (0.2529) (0.2481) (0.3914)

(0.1883) (0.1898) (0.1897) (0.1925)

and doubletons, the first reduction, eliminated 16 of 33 OTUs, and retained 95.2% of the total abundance. Also removing taxa occurring in only one (uniques) or two samples (duplicates), the second reduction, eliminated additional 3 taxa, and retained 92.7% of total abundance. Removal of all but the 9 (w25%) most abundant taxa, the third reduction, retained 86.3% of the total abundance. While the NMDS ordination of BrayeCurtis dissimilarities in the complete sample set required the elimination of two outlier samples, no outliers impaired the BrayeCurtis ordinations of any of the three reduced sample sets. None of the ordinations calculated from the MPD dissimilarities, from the complete set or any of the reductions, exhibited any outliers. In contrast, for each reduced set, ordinations of the UniFrac dissimilarities were impaired by multiple extreme outliers. These ordinations were only interpretable following outlier removal. The outliers in the BrayeCurtis ordination of the full sample set comprised two samples, with only singletons, or only singletons and doubletons (Table 7). Both of

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these samples contained only individuals from OTUs not found in any other sample. In contrast, the outlier samples from the UniFrac ordination of the full sample set (Samples A13, B8, and G3) consisted of taxa found in numerous other samples. The same was true for outliers in UniFrac ordinations of the reduced sample sets (Table 7). The ranking and strength of the strongest edaphic factor vectors remained consistent in BrayeCurtis ordinations, across reductions in the sample set (Fig. 2). Ranking and strength of vectors in the UniFrac and MPD ordinations changed in reductions of the data set. 3.3.3. Relationships of community characteristics with influential edaphic factors To further understand gradients that we identified, in relating edaphic factors with the differentiation between AM fungal communities, we examined the univariate relationships between key environmental variables and field-level community characteristics measured previously (Moebius-Clune et al., in press) (Table 2 and S1). Sand content was positively correlated (r ¼ 0.756, P ¼ 0.030) with the relative abundance of OTU31, identified as C. etunicatum, and negatively correlated (r ¼ 0.805, P ¼ 0.016) with the relative abundance of OTU33, identified as a member of the clade comprising C. etunicatum, Claroideoglomus claroideum, and Claroideoglomus luteum. None of clay, sand, Fe, or Mg was strongly related to any of the taxonomically based measures, save a negative correlation (r ¼ 0.79, P ¼ 0.021) between Mg and taxon richness. No significant correlation was found between Zn or AWC and taxon richness, Shannon diversity, or evenness, or between Mg and diversity or evenness. However, a non-significant trend was observed between increased AWC and Zn and increased richness and Shannon diversity, and between increased Mg and decreased richness and Shannon diversity. Clay content was marginally positively correlated (r ¼ 0.69, P ¼ 0.058) with phylogenetic diversity, and sand negatively correlated with phylogenetic diversity (r ¼ 0.76, P ¼ 0.028).

Table 5 Edaphic conditions at maize fields in New York State. For each edaphic factor, the measured values and, where used, the associated Cornell Soil Health Test Report scores are given. Edaphic factor

AgSt (%) AgSt score AWC (cm3 cm3) AWC score Sand (%) Clay (%) Silt (%) PenRes (kPa) PenRes score pH pH score P (mg g1) P score K (mg g1) K score Mg (mg g1) Fe (mg g1) Mn (mg g1) Zn (mg g1) OM (%) OM score ActC (mg g1) ActC score PMN (mg N g1 wk1) PMN score RootH (1e9 scale) RootH score

Field A

B

C

D

E

F

G

H

63.6 85 0.13 27 20.4 37.2 42.3 620 84 6.0 56 5.0 100 102.5 100 552.5 13.9 11.6 1.0 5.3 82 609.3 26 15.45 100 3.5 75

14.0 14 0.23 92 28.0 4.1 67.9 793 75 5.7 33 22.5 100 92.5 100 55.0 4.5 5.5 2.6 1.8 10 391.3 12 5.24 0 4.8 63

24.6 29 0.22 89 28.1 4.3 67.7 896 69 5.7 33 24.0 100 110.0 100 67.5 8.0 9.5 3.4 2.6 25 354.2 8 6.55 2 5.5 50

27.2 34 0.16 50 43.1 5.8 51.1 723 79 5.9 56 79.0 11 170.0 100 140.0 4.0 8.0 1.7 1.2 4 439.1 18 2.97 0 6.2 38

31.2 42 0.19 74 32.9 6.1 61.0 889 70 5.7 33 26.0 44 137.5 100 90.0 9.0 14.0 1.7 2.8 30 504.2 28 9.16 26 5.2 50

72.1 97 0.12 47 53.2 4.2 42.6 779 76 6.4 100 8.0 100 160.0 100 147.5 2.0 15.0 1.3 2.8 42 366.0 21 8.02 26 5.5 50

55.5 86 0.14 56 47.7 3.9 48.4 496 88 6.8 100 12.0 100 67.5 72 197.5 1.5 10.5 0.7 3.1 50 450.2 33 4.31 1 4.8 63

86.7 100 0.17 73 49.4 1.3 49.3 NA NA 6.3 100 20.0 100 267.5 100 220.0 1.0 25.0 0.7 3.9 70 748.5 80 14.97 100 3.8 75

Mean

CV

46.9 60.9 0.17 63.5 37.8 8.4 53.8 742.6 77.2 5.93* 63.9 24.6 81.9 138.4 96.5 183.8 5.5 12.4 1.6 2.94 39.1 482.8 28.3 8.3 31.9 4.9 58.0

0.52 0.53 0.22 0.33 0.30 1.31 0.18 0.18 0.08 0.61* 0.46 0.89 0.40 0.42 0.10 0.82 0.76 0.45 0.55 0.40 0.66 0.26 0.74 0.53 1.28 0.17 0.21

ActC, active (permanganate oxidizable) carbon; AgSt, aggregate stability; AWC, available water capacity; PenRes, penetration resistance; OM, organic matter; PMN, potentially mineralizable nitrogen; RootH, root health; CV, coefficient of variation; NA, not available; * mean and CV of pH calculated from [Hþ].

D.J. Moebius-Clune et al. / Soil Biology & Biochemistry 64 (2013) 191e199

1. 0 0.5

4. Discussion 4.1. Community dissimilarities In the present study, we found significant variation in AM fungal community composition in maize fields across a landscape in eastern New York, with 40% of the variation attributed to the field location. This finding is consistent with other observations regarding entropy and diversity in the focal assemblage, which showed that knowledge of the field location reduced the uncertainty in identity of randomly chosen individuals by about 40% (Moebius-Clune et al., 2013). Agreement between such substantially different methods suggests internal consistency in the methodological approaches. The remainder of the discussion focuses on explaining neutral and niche-related influences on the observed variation in these AM fungal communities. 4.2. Dispersal limitation Our tests of dispersal limitation show that this process does not play a significant role in AM fungal communities in maize fields.

Axis 2

-0.5

Sand

-1.0

B

AWC score Zn

-0.5

0.0

0.5

1.0

1.5

Silt AWC

Clay

-1

Field A Field B Field C Field D Field E Field F Field G Field H

Mg

0

1

2

C

1.0

1.5

ActC, active (permanganate oxidizable) carbon; AgSt, aggregate stability; AWC, available water capacity; PenRes, penetration resistance; OM, organic matter; PMN, potentially mineralizable nitrogen; RootH, root health; NS, not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

Silt AWC score

Clay Mg

-0.5

Axis 2

Zn

K -1.0

3.3.4. Partial Mantel test accounting for influence of edaphic factors To clarify the role of dispersal limitation in community compositional dissimilarities, in light of the edaphic factors identified by indirect gradient analysis, we reassessed the relationships between BrayeCurtis and UniFrac dissimilarities and distances between fields controlling for the relationship between distance and difference in sand content using partial Mantel tests. In both cases, BrayeCurtis and UniFrac, the relationship between dissimilarity and distance was non-significant (BrayeCurtis P ¼ 0.209, UniFrac P ¼ 0.113) when controlling for sand content.

K

-1 . 0 1. 0

-1.5

0.5

0.156** 0.161** 0.231*** 0.266*** 0.195*** 0.273*** 0.247*** 0.131* 0.146** 0.040 NS 0.081 NS 0.127* 0.187** 0.235*** 0.004 NS 0.281*** 0.161** 0.177** 0.252*** 0.212*** 0.196*** 0.183*** 0.148** 0.168** 0.205*** 0.185*** 0.158**

0.0

0.211*** 0.225*** 0.362*** 0.423*** 0.177** 0.352*** 0.385*** 0.217*** 0.245*** 0.056 NS 0.095 NS 0.100* 0.184** 0.213*** 0.034 NS 0.382*** 0.177** 0.192** 0.385*** 0.251*** 0.245*** 0.253*** 0.157** 0.231*** 0.295*** 0.231*** 0.197***

OM Fe Clay Mg

-0.5

0.116* 0.101* 0.173** 0.309*** 0.575*** 0.604*** 0.190*** 0.141* 0.144* 0.069 NS 0.131* 0.245*** 0.296*** 0.451*** 0.084 NS 0.483*** 0.544*** 0.275*** 0.148** 0.383*** 0.264*** 0.161** 0.180** 0.301*** 0.296*** 0.338*** 0.286***

Axis 2

MPD

1.0

UniFrac

-1.5

BrayeCurtis

-0.0

Method

-2.0

AgSt AgSt score AWC AWC score Sand Clay Silt PenRes PenRes score pH pH score P P score K K score Mg Fe Mn Zn OM OM score ActC ActC score PMN PMN score RootH RootH score

A

0.5

Edaphic factor

1. 5

Table 6 Fit of edaphic factor vectors to NMDS ordinations of BrayeCurtis, UniFrac, and mean patristic distance, MPD, dissimilarities. For each edaphic factor, including raw measured values and Cornell Soil Health Test Report scores, the r2 value is shown for the vector fit into ordination space.

0.0

196

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

Axis 1 Fig. 1. NMDS ordinations of maize field AM fungal communities. Two dimensional projections of NMDS ordinations calculated in three dimensions, for BrayeCurtis (A), UniFrac (B), and mean patristic distance, MPD, (C) dissimilarity measures. In each ordination, the six best fitting vectors of measured edaphic factor values or associated Cornell Soil Health Test Report scores are shown.

The only apparent trends that we identified were at the landscape level. These trends were weak, marginal, and disappeared when the most influential edaphic factor, sand, was taken into account. We would anticipate that with strong dispersal limitation, the measures reflecting relatedness per se, including both Unifrac and MPD, would show strong trends, even if the abundance based, structural dissimilarity measure (BrayeCurtis) did not. In population biology, genetic distance is expected to increase with increased physical separation where dispersal limitation is a significant force. In asexual organisms such as Glomeromycota, we would expect to find a similar pattern of phylogenetic divergence of multispecies communities with increasing distance.

D.J. Moebius-Clune et al. / Soil Biology & Biochemistry 64 (2013) 191e199 Table 7 Composition of extreme outlier samples removed from NMDS ordinations of maize field AM fungal communities. Sample indicates field site and sample number, composition indicates number of spores of each OTU present in the sample, frequency indicates the total number of samples in which each individual OTU was found. Method

Sample Composition

Frequency Taxonomic affiliation

Number of OTU individuals BrayeCurtis B3

1 2 1 1 1 3 1 7 1 1 2 2 1 1 5 1 1 2

G8 UniFrac

A13 B8 G3 A6 B1 B2

F10

OTU10 OTU12 OTU13 OTU28 OTU2 OTU21 OTU25 OTU25 OTU1 OTU21 OTU11 OTU25 OTU7 OTU25 OTU26 OTU11 OTU25 OTU4

1 1 1 1 1 8 15 15 2 8 3 15 6 15 3 3 15 1

Rhizophagus cf. clarus Rhizophagus cf. clarus Rhizophagus cf. clarus G. microaggregatum R. diaphanus Archaeospora schenckii Paraglomus occultum P. occultum Diversisporaceae Ar. schenckii Rhizophagus cf. clarus Paraglomus occultum R. irregulare P. occultum Ambispora sp. Rhizophagus cf. clarus P. occultum R. clarus

In contrast to our findings, weak dispersal limitation effects have been observed in AM fungal communities in a variable natural setting with numerous host plant species on the scale of tens of meters (Dumbrell et al., 2010), although these results were possibly confounded with pH effects. Substantially stronger dispersal limitation in AM fungal communities in new agricultural fields in Zimbabwe was observed at a scale of several kilometers (Lekberg et al., 2007). Dispersal and establishment of AM fungi is dependent both on a means of inoculum movement and on a sufficiently disturbed environment available for colonization (Camargo-Ricalde, 2002). In natural systems, animals, in particular rodents, are important Mg

Fe

Zn

Sand

Clay

Silt

0.7

Vector r 2

0.6 0.5 0.4 0.3 0.2 0.1

Data set

BC

Data set

Data set

UniFrac

MPD

3rd reduction

2nd reduction

1st reduction

Full

3rd reduction

2nd reduction

1st reduction

Full

3rd reduction

2nd reduction

Full

1st reduction

0

Ordination Fig. 2. Changes in the fit of edaphic factor vector to NMDS ordinations of maize field AM fungal communities with progressive removal of low-abundance taxa. The strength (r2) of the fit of vectors to BrayeCurtis, UniFrac, and mean patristic distance, MPD, ordinations, representing edaphic factors for the full data set, and reductions removing singletons and doubletons (1st reduction), additionally removing uniques and duplicates (2nd reduction), or retaining only the 9 most abundant taxa (3rd reduction). All factors which were ranked among the top 4 for one of the dissimilarity measures in the full data set are shown.

197

dispersal vectors for AM fungi (Fracchia et al., 2011; Janos and Sahley, 1995). In addition, Warner et al. (1987) found that windblown soil containing AM fungal propagules was a means of dispersal, and saw no correlation between the number of spores trapped from the wind and the distance from the inoculum source. In the agricultural environments that we surveyed, conditions are frequently conducive to successful in-migration of AM fungi, as soil is frequently, temporarily, bare or disturbed. In addition to animal and wind vectors, soil movement by way of human and machine traffic is also likely, allowing for potentially even greater avenues for dispersal. 4.3. Influential edaphic factors While we did not find evidence of dispersal limitation or isolation by distance, we were able to identify strong environmental gradients. Soil textural characteristics, namely clay and sand, appear as the most important vectors in the BrayeCurtis ordination. Similarly, silt and clay were identified as important by the phylogenetic measures of dissimilarity, with silt as the second-ranked vector in the UniFrac ordination, and clay as the second-ranked vector in the MPD ordination. Phosphorus and pH, which we anticipated to be among the stronger gradients, were substantially weaker, despite varying by at least as much as sand between fields. The influence of soil type on AM fungal community composition has been previously characterized (Oehl et al., 2010), but soil texture has typically not been identified as being of great importance. Anderson et al. (1984) addressed the role of texture in a prairiewetland transition, and ranked it lowest among the gradients observed, identifying instead pH, moisture, and plant host species as most important. Landis et al. (2004) identified a texture effect in an oak savannah ecosystem, but could not separate the effects of texture from plant community composition and soil N. These authors interpreted the texture effect as primarily due to soil porosity and nutrient ratios. In contrast to earlier studies, Lekberg et al. (2007) did find closely correlated influences of clay, moisture, and pH in a maize agricultural system in Zimbabwe, along with strong effects of soil organic C (SOC) and total N. This system was unfertilized maize on two soil types, in fields converted to agriculture less than 10 years earlier. Lekberg et al. (2007) argued that soil clay content partitioned niches among AM fungal families, and SOC partitioned niche space more finely among the Glomeraceae. While we have not separated the effects of environmental gradients at different taxonomic levels, the majority of the individuals in our fields were within Glomeraceae and Claroideoglomeraceae, with no Gigasporaceae present, even in the sandier soils. Moreover, on the basis of texture, we saw a shift between two fairly closely related dominant taxa within Claroideoglomus. Possible mechanisms of soil texture interaction with community composition and structure could entail differences in fungal growth forms between taxa and differences in how these varying forms extend through the soil matrix. Heavier, finer textured soils tend to be restrictive to plant root elongation growth more than lighter, coarser textured soils, and similar effects could be anticipated for fungal hyphal extension as well. Differences in this type of growth form have been observed at the family level, particularly between the Gigasporaceae and the Glomeraceae (Jakobsen et al., 1992; Smith and Read, 2008). It was primarily in the balance between these two families that the textural effects noted by Lekberg et al. (2007) manifested. However, no members of the Gigasporaceae were found in our study system, and the large majority (82.7%) of the variation between samples, which we observed, was within the families Glomeraceae and Claroideoglomeraceae (Glomerales). Furthermore, while penetration resistance measurements were

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available for only seven of eight fields, vectors of these values ranked relatively low, indicating that penetration resistance was not well associated with community compositional differences, and therefore hyphal extension limitation is an unsatisfactory mechanistic explanation of the relationship between soil texture and community composition. Instead, soil moisture relations may offer a more satisfactory interpretation of possible textural effects. Soil moisture and texture often are related, as coarser soils tend to be better drained and likely to fluctuate more in water potential. A vector representing available water capacity (AWC) score, which reflects the curvilinear nature of the sufficiency of available water capacity for supporting plant growth in these soils (a sigmoidal “fuzzy threshold” function), ranked highly in both the UniFrac and MPD ordinations. The AWC score was the strongest identified gradient in both of these dissimilarity metrics when examining only the w93% of fungal individuals found in more than two samples, followed in both cases by silt content. A functional change in plant response to soil water stress has been reported in mycorrhizal plants, in a manner that differed between fungal species (Augé, 2001). If the apparent community compositional partitioning along a texture gradient is due to soil moisture effects, it seems possible that a feedback with host performance could favor fungi that better facilitate host response to water stress. The phylogenetic measures used in our study varied in the relative rankings of the strongest environmental vectors, both in comparison with each other and with de-noising of the sample set. However, both MPD and UniFrac identified in addition to soil texture, two particular nutrient gradients, Zn and Mg. Mg content was identified as the strongest gradient by the MPD measure when viewing the entire sample set, or after removal of singletons and doubletons. Zn and Mg tended to be ranked fairly similarly by UniFrac, and either just ahead of or just after silt content, depending on the number of rare taxa retained. Although the CSHTR minor element score was maximal for all of the fields, higher Zn content tended to be associated with higher richness and taxonomic diversity, while Mg exhibited the opposite trend. Many observations about AM fungal communities have suggested relationships between composition or functional performance and nutrient content, but the focus generally is on phosphate content, or on comparisons between fertilized and non-fertilized conditions. Nevertheless, higher nutrient content typically is associated with reduced colonization levels, and often with lower AM fungal diversity, although this pattern is strongly variable (Smith and Read, 2008). The Mg gradient observed in our focal system may be of this nature, limiting the breadth of the fungal community.

likely related to such sensitivity, the repeated appearance of extreme outliers in ordinations was likely not. Ordination of UniFrac dissimilarities produced several outliers extreme enough (thousands of IQR widths outside of the IQR of the rest of the sample cloud) to render the ordinations uninterpretable except as a representation of the differences between these few samples and the rest collectively. This was seen even after exclusion of those taxa most interpretable as noise in the data set, and even with fairly extreme reduction in the sample set (to only the 9 most frequent and abundant taxa). In addition, the outlier samples did not contain only rare or infrequent taxa, or even taxa highly phylogenetically divergent from the rest. Instead, taxa appearing in numerous other samples, whether occurring alone or with other taxa, comprised outlier samples. The MPD measure, which correlated quite well with the UniFrac measure, and revealed similar gradients, did not exhibit any of these issues. MPD produced useful ordinations with no outliers at all levels of taxon inclusion, and was stable in addressing spatial structuring of community differences, even with the removal of the few very low abundance individuals. Overall, MPD is a simple, easily interpretable metric. It may be advisable when analyzing numerous samples, each with several individuals, to use a metric such as MPD for phylogenetic ordination as suggested by Webb et al. (2002) and Webb et al. (2008b) to identify environmental gradients affecting the soil microbial community. 5. Conclusion Multiple patterns in community structure and phylogenetic relatedness suggested that niche-related factors influence AM fungal community differentiation among fields in New York State. We did not find strong evidence of dispersal limitation or isolation by distance within fields or across the landscape. We identified several environmental gradients apparently related to community compositional differences. Soil texture, in particular, seemed strongly related to community differences, and we speculate that this pattern may be explainable by the relationship between texture and soil moisture availability. Phylogenetic ordination techniques identified gradients that, while less pronounced than those identified using more traditional taxonomic compositional metrics, added interpretive value in relating community differentiation to environmental factors. The mean pairwise patristic distance (MPD) metric was more consistent and stable with our data than was the UniFrac metric. While AM fungal communities in natural settings are likely strongly influenced by host plant community and soil nutrient gradients, in agroecological settings, with a common host and management that maintains high available nutrient levels, soil textural effects may outweigh the impact of soil nutrients.

4.4. Methodological considerations Acknowledgments The incorporation of phylogenetic information into our analyses of AM fungal communities in New York State fields enabled greater insight into the relationships between organisms and their environment. The environmental gradients suggested by UniFrac and MPD ordinations in the maize field AM fungal communities were not as strong as those identified for the BrayeCurtis dissimilarities. Nevertheless, they contributed toward the interpretation (both confirmation and explanation) of patterns observed in the community composition using the BrayeCurtis measure. While the two measures revealed similar trends, and were together informative in confirming and interpreting the strong textural gradients, they were measured differently and exhibited different levels of stability in ordinations and sensitivity to low abundance taxa. Fukuyama et al. (2012) identified in a comparison of UniFrac and a patristic distance-based measure, a sensitivity to low abundance OTUs. While the change in landscape-level dispersal limitation signal was

We are grateful to Bryant Adams for assistance in software implementation of Mantel tests of plenary within-field distance and dissimilarity relationships. We thank two anonymous reviewers for helpful comments. This research was supported by the Cornell University Agricultural Experiment Station federal formula funds, Project NYC-153441, received from the National Institutes for Food and Agriculture, U.S. Department of Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.soilbio.2012.12.014.

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