Interactions between soil properties, fungal communities, the soybean cyst nematode, and crop yield under continuous corn and soybean monoculture

Applied Soil Ecology xxx (xxxx) xxxx

Contents lists available at ScienceDirect

Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil

Interactions between soil properties, fungal communities, the soybean cyst nematode, and crop yield under continuous corn and soybean monoculture Noah Stroma, Weiming Hub, Deepak Haarithc, Senyu Chend, Kathryn Bushleya,* a

Department of Plant and Microbial Biology, University of Minnesota, 822 Biological Sciences, 1445 Gortner Avenue, St. Paul, MN 55108, USA Entomology and Nematology Department, University of Florida, Gainesville, FL, USA c Department of Plant Pathology, University of Minnesota, Saint Paul, MN, USA d Southern Research and Outreach Center, University of Minnesota, Waseca, MN, USA b

ARTICLE INFO

ABSTRACT

Keywords: Crop rotation Monoculture yield decline Soybean cyst nematode Nematophagous fungi Arbuscular mycorrhizae Phosphate-solubilizing fungi Structural equation modeling

Corn (Zea mays) and soybean (Glycine max) production forms an integral part of economies worldwide, but yields are limited by biotic and abiotic factors associated with short rotations and long-term monocultures. The objectives of this study were (i) to investigate the role of corn-soybean crop rotations and continuous monocultures in shaping bulk soil fungal communities, ii) to identify fungal taxa or functional guilds correlated with SCN density, and (iii) to characterize relationships between biotic and abiotic factors and their effects on corn and soybean yields. The study utilized a long-term rotation site with corn and soybean planted in annual rotation, five-year rotation, and long-term monoculture. High throughput sequencing of the ITS1 region of fungal rDNA revealed that soil fungal community structure varied significantly by crop sequence, with fungal communities under five consecutive years of monoculture becoming progressively similar to corresponding communities in long-term monoculture plots. Total fungal alpha diversity was greater under corn, but patterns of diversity and relative abundance of specific fungal functional guilds differed by crop, with more nematophagous fungi proliferating under soybean and more arbuscular mycorrhizal fungi (AMF) proliferating under corn. The relative abundance of nematode-trapping fungi and several putative nematode egg parasites was positively correlated with SCN density at several time points, suggesting that these fungi may proliferate as a result of the availability of the SCN as a nutrition source. Soil properties also varied by crop sequence, with higher pH and P under continuous soybean and higher Fe, Mn, and Cu under continuous corn. Lower levels of P corresponded with the relative abundance of several orders of fungi with roles in P uptake and transfer to plants (Glomerales, Paraglomerales, and Sebacinales), while higher P levels corresponded with the relative abundance of Mortierellales, a fungal order containing phosphate-solubilizing fungi. Structural equation modeling identified the SCN and soil nitrogen as the most important variables explaining soybean yield and fungal pathogens of corn and soil nitrogen as the most important variables explaining corn yield.

1. Introduction Corn and soybean accounted for 53% of total acreage planted to principal crops and 49% of principal crop production in the United States in 2016 (U.S. Department of Agriculture National Agricultural Statistics Service, 2017). However, both crops suffer yield declines under continuous monoculture, and crop rotation is critical for maintaining their productivity (Crookston et al., 1991). Various pathogens are thought to accumulate over continuous monoculture, contributing to negative plant-soil feedbacks that result in monoculture yield decline

(Bever et al., 1997; Mills and Bever, 1998). For example, a previous study at our study site showed that the density of a major soybean pathogen, the soybean cyst nematode (Heterodera glycines, SCN), increased over consecutive years of soybean monoculture, while populations of the plant parasitic nematodes Pratylenchus and Helicotylenchus increased under corn, correlating with yield declines of their respective host crops (Grabau and Chen, 2016a, 2016b). The accumulation of fungal pathogens of soybean and corn have also been implicated in monoculture yield decline (Gracia-Garza et al., 2002; Jirak-Peterson and Esker, 2011; Kozhevnikova, 1975; Pedersen and Grau, 2010;

Abbreviations: AMF, arbuscular mycorrhizal fungi; CLR, centered log ratio; ITS1, internal transcribed spacer 1; OTU, operational taxonomic unit; SCN, soybean cyst nematode ⁎ Corresponding author. E-mail address: [email protected] (K. Bushley). https://doi.org/10.1016/j.apsoil.2019.103388 Received 22 April 2019; Received in revised form 2 October 2019; Accepted 15 October 2019 0929-1393/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Please cite this article as: Noah Strom, et al., Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2019.103388

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

Rousseau et al., 2007; Rupe et al., 1997). Even communities of arbuscular mycorrhizal fungi (AMF), which are canonically thought to improve crop yields by providing phosphorus (P) in exchange for carbon (Siddiqui and Pichtel, 2008), have been shown to become less beneficial with repeated planting of the same plant host (Bever, 2002). Johnson et al. (1992) hypothesized that the species of AMF that accumulate under monoculture may contribute to yield declines in corn and soybean in our study system. While continuous monoculture is often associated with the accumulation of host-specific plant pathogens, it can also lead to the accumulation of organisms antagonistic to specific plant pathogens, including plant parasitic nematodes (Kerry and Crump, 1998; Shipton, 1973). For example, Kerry (1988) found that fungi that were parasitic on cereal cyst nematode (Heterodera avenae) eggs accumulated over long-term cereal monoculture in fields infested with this plant-parasitic nematode. However, the response of fungal parasites of the SCN to soybean monoculture and crop rotation has not been well-studied. Previous studies have suggested that natural predators or parasites of nematodes track the density of their host (Persmark, 1996). An important basic question is whether nematophagous fungi track the density of the SCN in the corn-soybean agroecosystem. Among nematophagous fungi three guilds are recognized: i) near-obligate endoparasites of free-living nematodes, ii) nematode egg parasites, and iii) nematode-trapping fungi (Chen and Dickson, 2012). Understanding which taxa in these nematophagous guilds positively correlate with SCN density may help to identify potential SCN parasites that could be applied or managed through crop rotation to control the SCN. Our study utilized a unique long-term research site at which corn and soybean have been planted under continuous long-term monoculture, annual rotation, and 5-year rotations since 1982 (Crookston et al., 1991). Soil nutrients, in particular soil levels of P, have been observed to decrease over continuous corn monoculture at this site (Copeland and Crookston, 1992; Johnson et al., 1991) and have been implicated in yield penalties of both corn and soybean in other studies (Bender et al., 2015; Xin et al., 2017). However, although soil nutrients and other soil properties such as soil moisture (Copeland et al., 1993), soil structure (Nickel et al., 1995), and crop residue volume and chemistry (Crookston et al., 1988; Crookston and Kurle, 1989; Nickel et al., 1995) have been shown to contribute to yield declines, they cannot full explain the yield declines observed under continuous monoculture. Therefore, biotic factors, including fungi, also likely play a role. The objectives of this study were (i) to investigate the role of cornsoybean crop rotations and continuous monocultures in shaping bulk soil fungal communities, (ii) to identify fungal taxa or functional guilds correlated with SCN density, and (ii) to explore relationships between abiotic and biotic factors in this system and identify explanatory factors in corn and soybean yield. We hypothesized that (i) different communities of crop host-specific pathogens and AMF would proliferate under each crop and would correlate with decreased yields; (ii) nematophagous fungi would increase in diversity and relative abundance over continuous soybean monoculture, corresponding with an increase in SCN density; (iii) soil nutrients that became depleted over continuous monoculture of either crop would be correlated with yield declines.

Table 1 Corn and soybean crop sequences in a long-term crop rotation study site established in 1982 in Waseca, Minnesota, USA. Crop sequences include 5 consecutive years of corn after the 5th year of soybean (C1-C5); 5 consecutive years of soybean after the 5th year of corn (S1-S5); soybean in annual rotation with corn (Sa); corn in annual rotation with soybean (Ca); long-term Bt corn (Cc) and SCN-susceptible soybean (Ss) monocultures; non-Bt Corn (Cn) and SCN-resistant soybean (Sr) monocultures since 2010. Crop sequence Treatments

2015

2016

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

C4 C3 C2 C1 S5 S4 S3 S2 S1 C5 Cc Ss Sa Ca Cn Sr

C5 C4 C3 C2 C1 S5 S4 S3 S2 S1 Cc Ss Ca Sa Cn Sr

(Minnesota Department of Natural Resources, 2019). In 2015 and 2016, the annual average temperature at the study site was 8.2 °C and 9.2 °C, respectively, and the annual rainfall was 116 cm and 143 cm, respectively (Southern Research and Outreach Center, 2015, 2016). 2016 was the wettest year on record at the study site (Minnesota Department of Natural Resources, 2017). 2.2. Experimental design Corn and soybean have been planted in continuous cropping sequences at this experimental site since 1982. The experimental design was the same as in previous studies conducted at this site (Grabau and Chen, 2016a, 2016b; Hu et al., 2018) and is described briefly, here. The crop sequences in this study were continuous Bt corn (Dekalb 50–82) (Cc) and SCN-susceptible soybean (Pioneer 91Y90) (Ss) monocultures, 5-year rotations in which five years of Bt corn (C1-C5) were followed by five years of SCN-susceptible soybean (S1-S5), corn-soybean annual rotations (Ca and Sa), and continuous non-Bt corn (Dekalb 50–67 in 2015; Dekalb 46-18 in 2016) (Cn) and SCN-resistant soybean (Pioneer 92Y22 in 2015; Asgrow 1935 in 2016) (Sr) monocultures (Table 1). There were four replicate plots for each of the sixteen crop treatments, and treatment plots were arranged in the field in a randomized complete block design. For both soil properties and fungal communities, one pooled soil sample was used for each plot, with a total of 384 samples (16 treatments, 4 replicates per treatment at each of 6 time points). Each experimental plot was 4.57 m wide by 7.62 m long and contained six rows of plants, each. Plots were managed by conventional tillage practices consisting of fall chisel plowing and field cultivation prior to planting. All crops were resistant to glyphosate (Roundup) herbicide, which was used at a rate of 2.2 kg ha−1 to prevent weed growth. Corn and soybean plots were sprayed with Endigo insecticide at a rate of 245 g ha−1 at midseason in 2015 for aphid control. No insecticides were used in 2016. Corn plots were fertilized with nitrogen as urea at a rate of 224.4 kg ha−1 at planting in 2015 and 2016. No phosphorus-containing fertilizer was applied in 2015 and 2016, but P-K fertilizer was applied at a rate of 89.7 and 134.5 kg ha−1, respectively, to all plots in 2014.

2. Materials and methods 2.1. Site description The study was conducted at the University of Minnesota Southern Research and Outreach Center in Waseca, Minnesota, USA (44°04′ N, 93°33′ W) on a Nicollet clay loam (fine-loamy, mixed, mesic Aquic Hapludoll) (Soil Survey Staff, 2014). This site is representative of agricultural fields in southern Minnesota, a highly productive growing region for corn and soybean (Meade et al., 2016). This region experiences a continental climate with hot summers and cold winters 2

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

2.3. Sample collection

Minneapolis, MN, USA). Bulk soil fastq files from identical samples in separate lanes were merged using the fastq-cat.pl script in the Gopherbiotools package (Garbe, 2015). Preprocessing and merging of reads, OTU clustering, filtering, and curating were performed using the AMPtk v1.1.0 pipeline with default parameters, except where noted (Palmer et al., 2018). The trim length for Illumina reads was set to 250 bp for pre-processing. Processed and merged reads were clustered in AMPtk using the "dada2" option. The OTU table was filtered using AMPtk's default index bleed filtering rate of 0.05% and curated using LULU v1.1.0, which merges "split" OTUs, resulting in better alpha diversity estimates (Frøslev et al., 2017). Based on the recovery of taxa from our fungal mock community, a synthesis of four different methods and a custom R script (available at https://github.com/stro0070/OTU-taxonomy-assignment) was used for taxonomy assignment (Supplementary Methods). The OTU table with taxonomy was filtered to include only fungal OTUs and members of the synthetic mock community. The AMPtk pipeline resulted in 12,068 OTUs, of which 11,701 remained after filtering to remove non-fungal OTUs and adding back synthetic mock community OTUs. The final OTU table was filtered to remove OTUs with fewer than 10 reads across the entire table, after which 8587 OTUs remained, of which 8491 were present in bulk soil samples. After filtering, sequence depth for bulk soil samples ranged from 16,435 to 205,157 reads with an average sequence depth of 63,947. The rarefaction curves for most samples began to level off by around 30,000 reads, indicating that the sampling depth was adequate to capture most of the fungal diversity (Fig. S1). The taxonomy assignment pipeline was able to capture a majority of mock community fungal taxa, with 100% and 83% of taxa identified in two PCR technical replicates of the mock community (Table S2). Ten out of twelve synthetic mock community sequences were detected in our synthetic mock community sample after index-bleed filtering (Table S3) (Palmer et al., 2018). Technical replicates of bulk soil samples resulted in communities that were remarkably homogeneous, with 62% of variation between fungal communities being explained by the sample of origin (Fig. S2). Additional details of fungal and synthetic mock community recovery and technical replicates analysis are described in the Supplementary Methods. Fungal taxonomy is currently undergoing substantial revision. As of the date of this writing, some phylum names in the UNITE database are outdated, and subphyla names are not included (UNITE Community, 2017). We decided to retain the UNITE database taxonomy assignments without modification due to the complex nature of the effort that would be required to update them. Thus, we report, for example, on the abundance of Glomeromycota rather than Glomeromycotina and on Mortierellomycota instead of Mortierellomycotina (Spatafora et al., 2016). The trapping fungi, egg parasite, and endoparasite nematophagous guilds were defined as in Hu et al. (2018) by performing literature searches using keywords: "soybean cyst nematode", "biological control," "fungi," "nematophagous", "trapping", "predator," "endoparasite", and "egg parasite." Host-specific pathogens of corn and soybean were also identified through literature searches using the keywords: "corn," "maize," "Zea mays," "soybean," "Glycine max," "pathogen," and "disease." Taxa detected in the amplicon sequencing data classified in each nematophagous guild or identified as corn or soybean pathogens are listed in Table S4.

Bulk soil samples were collected in the spring (at planting), midseason (2–3 months after planting), and fall (at harvest). A total of 20 soil cores were taken at regular intervals from the two central rows of each plot using a 2.54 cm-diameter probe sunk to a depth of 20 cm. Soil samples were stored in a cold room at 4 °C until further processing on the same day. Soil cores from each plot were pooled, pushed by hand through a 5 mm mesh screen to break apart cores into smaller aggregates, and thoroughly mixed by hand. A subsample of 100 cm3 of homogenized soil was used for SCN egg quantification. Soil properties analysis was performed on 100 g of homogenized soil collected in the spring by the Research Analytic Laboratory at the University of Minnesota. Soil P was measured using the Bray method (Bray and Kurtz, 1945). The remaining homogenized soil (250 g) was stored in a plastic bag at −80 °C for later processing. 2.4. SCN egg density quantification and yield measurement The density of SCN eggs in the bulk soil was determined following the methods described in Hu, et al. (2017). Briefly, cysts were separated from 100 cm3 of bulk soil by elutriation followed by centrifugation in a 63% (w/v) sucrose solution. Cysts were crushed to release eggs (Faghihi and Ferris, 2000). Eggs were then collected in water and quantified by examining a subsample of egg suspension with an inverted microscope. This number was used to calculate the total number of eggs in 100 cm3 of soil. Yield was measured from 6.1 m of the two central rows of each experimental plot using a plot combine. 2.5. DNA extraction, amplification, and sequencing Fifty grams of soil from each 250 g soil sample from each plot was homogenized further in a coffee grinder, and DNA was extracted from 0.25 g of this soil using a MoBio PowerSoil® DNA Isolation Kit. The coffee grinder was washed with soap and water and sterilized with 70% ethanol between samples. Twenty microliters of DNA per sample were submitted to the University of Minnesota Genomics Center (Saint Paul, MN, USA) for amplification and sequencing. Additional samples to assess the quality and accuracy of the amplification and sequencing were also included. These included a fungal mock community, a synthetic mock community (Palmer et al., 2018), technical replicates consisting of replicate DNA extractions from the same soil sample, and negative controls in which no soil was added to the extraction kit. Our biological (fungal) mock community contained 36 fungal isolates from across several major phyla (Ascomycota, Basidiomycota, Chytridiomycota, and Mucoromycota) and included both closely related and more distantly related taxa to optimize the accuracy and precision of taxonomy assignments (Table S1). DNA for mock community members was isolated from pure cultures or vouchered herbarium specimens, and the full length ITS was sequenced and compared to public databases to verify taxonomic identity. More details on the preparation and analysis of mock communities is provided in the Supplementary Methods. A two-step dual-indexed amplification method was used for amplifying fungal internal transcribed spacer 1 (ITS1) regions of fungal rDNA (Gohl et al., 2016). Paired-end sequencing (2 × 250 bp) was carried out using a MiSeq 500 cycle v3 kit on a total of four lanes. Illumina sequencing resulted in a total 47,902,914 reads from bulk soil samples that passed Illumina quality control. A total of 7 bulk soil samples, which had fewer than 20,000 reads after quality control, were re-submitted for amplification and sequencing, resulting in an additional 744,408 quality reads.

2.7. Statistical analyses All statistical analyses were performed in RStudio v 3.5.0 (R Core Team, 2018; RStudio Team, 2016). All p-values associated with multiple hypothesis testing were corrected by the false discovery rate (FDR) procedure (Benjamini and Hochberg, 1995). Significant differences in yield and soil properties across crop sequences were detected using ANOVA, and relationships between individual soil properties and yield were tested using the "lm" function on

2.6. Bioinformatics processing Fastq files of reads that passed Illumina quality control were stored and processed at the Minnesota Supercomputing Institute (MSI, 3

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

Fig. 1. Corn (A) and soybean (B) yields and SCN density (C) across crop sequences and SCN-susceptible soybean yield by SCN density (D) in 2016. Error bars show one standard error above and below the mean.

the linear model, Yield ∼ Block + Soil Property, followed by ANOVA. Soil properties were standardized to z-scores prior to regression analyses. Due to unequal variance that could not be corrected through transformation, the Kruskal-Wallis test (Kruskal and Wallis, 1952) was used to compare SCN density across crop sequences. Differential abundance analyses were performed at the phylum and species level and for fungal functional groups. Due to the high false discovery rate associated with using proportions for differential abundance testing (Weiss et al., 2017), read counts were instead transformed to centered log ratios (CLRs) (Aitchison, 1982; Martín-Fernández et al., 2003), which are associated with a much lower false discovery rate (Gloor et al., 2017). This is the same approach used in the R package “ALDEx2” (Gloor, 2018) and has been used in other metabarcoding studies (Lee et al., 2014; Mcdonald et al., 2018). CLRs of taxa, corn and soybean pathogens, and nematophagous guilds with equal variance according to Levene's test were compared across crop sequences using ANOVA on the linear model, Y ∼ Block + Crop Sequence for each sampling time point (e.g. Spring 2015). For CLRs that failed Levene's test, the Kruskal-Wallis test (Kruskal and Wallis, 1952) was used, instead. Post-hoc comparisons were made using Tukey's HSD test (Tukey, 1949) or the "kruskal" function with FDR correction in the R package, "agricolae" (Mendiburu, 2017). Spearman correlation tests were used to test for correlations between CLRs of taxa or functional groups of fungi and soybean and corn monoculture year, SCN density, and yield in each sampling time point. Non metric multidimensional scaling (NMDS) (Kruskal, 1964) was performed using the "metaMDS" function in vegan (Oksanen et al., 2016) on a Bray-Curtis dissimilarity matrix generated from Hellingertransformed OTU read counts (Legendre and Gallagher, 2001). The "Adonis" function in vegan was used to parse the effects of season and crop sequence. In order to examine relationships between all measured soil properties and fungal orders, distance-based redundancy analysis (dbRDA) was performed using the "capscale" function in vegan (Legendre and Anderson, 1999; Oksanen et al., 2016) using an OTU

table collapsed by order with the QIIME2 "collapse" plugin (Caporaso et al., 2010). A type III ANOVA was performed on the model used in the redundancy analysis to identify the proportion of variation in beta diversity of the fungal community explained by each soil property. Alpha diversity metrics were calculated in the "Phyloseq" package (McMurdie and Holmes, 2013). Effects of crop sequence on alpha diversity were tested using ANOVA. Rarefaction curves were generated using the "rarecurve" function in vegan (Oksanen et al., 2016). Structural equation modeling was carried out using the "Lavaan" package (Rosseel, 2014), following the methods used in Borer et al. (2012). Briefly, we began with a conceptual meta-model based on literature relating to monoculture yield decline. The meta-model was designed to examine the effects of continuous monoculture (exogenous factor) on 1) plant pathogens (host-specific fungi and the SCN), 2) soil properties (N, P, and pH), and 3) fungal plant mutualists (AMF, phosphate-solubilizing fungi, and nematophagous fungi) and to test hypotheses regarding the effects of each of these factors on crop yield. Initial full models for each crop based on this conceptual meta-model were first identified that closely fit the data for corn and soybean yield, with years of continuous monoculture as an exogenous variable and soil properties and the relative abundance of crop host-specific fungal plant pathogens, the SCN, nematophagous fungi, AMF, and fungi within the genus Mortierella as endogenous variables. Soil N was also initially modeled as an exogenous variable for corn but not for soybean, as nitrogenous fertilizer was used only in corn plots. We first tested hypotheses on the full model by removing the links from fungal pathogens to yield, soil properties to yield, and fungal mutualists to yield and conducting likelihood ratio tests. If the model with the link removed was not significantly different from the full model according to this test and if the R2 value for yield was not reduced, the hypothesis was not supported, and the link was removed. Model reduction was performed by stepwise removal of additional links in order of their statistical significance, based on the same criteria as for hypothesis testing. A detailed description of the process used to create the structural equation 4

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

models can be found in the Supplementary Methods.

"Purpureocillium" against the UNITE database (UNITE Community, 2017) revealed that known egg-parasitic taxa, Clonostachys rosea (Zhang et al., 2008) and Purpureocillium lilacinum (Song et al., 2016), respectively, were their most likely identities.

3. Data Accession The raw sequences from bulk soil samples were deposited into the NCBI database (Accession number: PRJNA484933).

4.4. Variation in fungal communities in relation to crop sequences, SCN density, and soil properties

4. Results

Adonis, a non-parametric multivariate ANOVA used to identify sources of variation between communities (Anderson, 2001; Oksanen, 2015), revealed significant effects of year (R2 = 0.03, p = 0.001), season (R2 = 0.06, p = 0.001), crop sequence R2 = (0.15, p = 0.001), season:year (R2 = 0.03, p = 0.001), and crop sequence:year (R2 = 0.04, p = 0.001) on fungal community dissimilarity. Long-term monoculture communities of each crop showed the clearest separation by crop in ordination space, with long-term soybean monoculture (Ss and Sr) communities on opposite sides of the NMDS plots from longterm corn monoculture (Cc and Cn) communities (Figs. 2A and S4A). Between these extremes, communities associated with each crop generally became more similar to corresponding long-term monoculture communities with increasing years of monoculture (Figs. 2A and S4A). Distance-based redundancy analysis showed that a combination of SCN density and soil properties explained 33% and 35% of variation between soil fungal communities at the ordinal level in midseason 2016 and 2015, respectively, with the largest proportion of community dissimilarity explained by soil P (11%), K (3.7%), and pH (3.7%) in 2015 and by soil P (7.9%), pH (3.2%), and Mn (3.1%) in 2016 (Figs. 2B and S4B). Adding a crop sequence term to the model explained an additional 25% of community variation in both sampling years and resulted in a significantly better model than soil properties, alone, based on a likelihood ratio test (χ2 = 0.50, df = 15, p < 0.001 for 2015 data , χ2 = 0.49, df = 15, p < 0.001 for 2016 data). Soil P corresponded with the relative abundance of Mortierellales, an order containing several species of phosphate-solubilizing fungi (Li et al., 2018), and negatively corresponded with the relative abundance of the AMF order Glomerales (Figs. 2B and S4B), as well as with Paraglomerales and Sebacinales (data not shown). Pearson correlation tests also revealed positive correlations between soil P and Mortierellales (r = 0.37, 0.22; p = 0.002, 0.08 in midseason 2015, 2016) and negative correlations between soil P and Glomerales (r = −0.50, −0.16; p < 0.001, 0.20 in midseason 2015, 2016), Paraglomerales (r = −0.24, −0.18; p = 0.06, 0.15 in midseason 2015, 2016), and Sebacinales (r = −0.39, −0.44; p = 0.001, < 0.001 in midseason 2015, 2016), an order containing several fungi involved in P acquisition by plants (Yadav et al., 2010).

4.1. Crop yields and soybean cyst nematode density Corn yields were significantly higher in years following soybean (C1 and Ca) (Figs. 1A and S3A). By contrast, soybean did not have significantly higher yields in the first year following five years of corn (S1) or in annual rotation with corn (Sa) compared to other soybean crop sequences (Figs. 1B and S3B). In 2015, SCN-resistant soybean (Sr) had significantly greater yields than SCN-susceptible soybean (Ss), and longterm SCN-susceptible soybean (Ss) had significantly lower yields than SCN-susceptible soybean under shorter-term monocultures (S3 and S5) (Fig. S3B). SCN density differed significantly by crop sequence in both 2015 and 2016 (Figs. 1C and S3C). In general, SCN egg density decreased with increasing years of corn monoculture and increased with increasing years of SCN-susceptible soybean monoculture and was lower in long-term SCN-resistant soybean (Sr) plots compared to long-term SCN-susceptible (Ss) soybean plots (Figs. 1C and S3C). Significant (p < 0.01) negative correlations between SCN-susceptible soybean yield and midseason and fall SCN density were observed in 2016 (Fig. 1D) but not in 2015 (Fig. S3C). 4.2. Soil properties Most individual soil properties showed increases over either soybean or corn monoculture, with organic matter (OM), Fe, Mn, Cu, total nitrogen (N), and total organic carbon (TOC) increasing under corn and pH and phosphorus (P) increasing under soybean (Tables 2 and S5). However, only P, Mn, and Cu were found to differ significantly by crop sequence, with levels of soil P under continuous soybean (Ss and Sr) over twice those observed under continuous corn (Cc) (Tables 2 and S5). Correlations between individual soil properties and yields of corn and soybean typically had opposite signs for the two crops (Table S6). Soybean yields in 2016 were significantly (p < 0.05) positively correlated Fe, Mn, Cu, and N and with TOC at a significance level of p = 0.062 and were significantly (p < 0.05) negatively correlated with C:N, P, and pH (Table S6). Corn yields in 2016 were significantly (p < 0.05) positively correlated with TOC, N, and OM and with P at a significance level of p = 0.065. Similar patterns were found for 2015 data (Table S6).

4.5. Alpha diversity Total observed OTUs were significantly greater under corn than under soybean, overall, in 2015 (p = 0.01) and 2016 (p = 0.003), though diversity under corn was not significantly greater than under soybean for all crop sequences (Fig. S5). Alpha diversity patterns differed for different fungal guilds, with AMF becoming increasingly diverse under continuous corn and nematode-trapping and nematode eggparasitic fungi becoming more diverse under continuous soybean (Fig. S5).

4.3. Fungal community profiles Averaging across all sampling time points and crop sequences, a majority of reads belonged to OTUs assigned to phylum Ascomycota (56%), with Basidiomycota (14%), Mortierellomycota (13%), and Chytridiomycota (2%) having the next most abundant read counts. A significant portion of reads (12%) were assigned to kingdom Fungi but not to any fungal phylum. Fewer than 1% of reads, each, were assigned to Blastocladiomycota, Calcarisporiellomycota, Entomophthoromycota, Entorrhizomycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota, and Zoopagomycota. In terms of nematophagous fungal guilds, 3.7% of total reads belonged to OTUs classified as nematode egg parasites, while a much smaller proportion belonged to OTUs classified as nematode-trapping fungi (0.07%) and nematode endoparasites (0.01%) (Table S4). Additional BLAST searches of OTUs identified as "Clonostachys" and

4.6. Differential abundance of fungi by crop sequence Two main patterns were observed regarding the effect of crop sequence on the relative abundance of fungal taxa and nematophagous guilds: i) an increase in relative abundance over years of soybean monoculture and a decrease over years of corn (soybean-associated fungi), and ii) an increase in relative abundance over years of corn monoculture and a decrease over years of soybean (corn-associated fungi). 5

13 (2.9)

16 (1.2)

15 (2.5)

28 (4.0)

35 (4.7)

16 (4.7)

14 (1.2)

< 0.001

S3

S4

S5

Ss

Sr

Ca

Sa

ANOVA p-value

11 (1.7)

Cn

16 (2.1)

8 (0.9)

Cc

S2

16 (2.4)

C5

11 (2.8)

14 (2.1)

C4

S1

12 (2.3)

C3

6

0.50

140 (7)

124 (3)

125 (2)

126 (2)

139 (15)

121 (7)

131 (10)

134 (7)

126 (14)

132 (8)

140 (10)

132 (10)

144 (9)

132 (3)

148 (15)

0.30

86 (24)

61 (14)

58 (11)

38 (6)

80 (18)

81 (10)

47 (13)

97 (9)

73 (24)

119 (23)

115 (30)

96 (12)

76 (28)

54 (18)

86 (26)

0.005

44 (4)

45 (4)

37 (3)

30 (1)

45 (4)

47 (2)

37 (7)

48 (2)

42 (4)

60 (3)

60 (4)

46 (2)

43 (7)

41 (5)

40 (8)

0.64

6.2 (1.2)

5 (0.25)

7 (1.1)

5.3 (0.29) 4.9 (0.5)

4.3 (0.54) 6 (0.7)

5 (0.57)

7.2 (1.8)

6.6 (1.1)

6.7 (2.2)

7.5 (1.1)

6.1 (1.7)

5.4 (0.8)

5.6 (0.66) 7.2 (1.3)

16 (3.4)

47 (2)

C2

94 (22)

20 (3.2)

C1

135 (14)

P K Fe Mn Zn ————————————————————————————————————mg −1 kg ————————————————————————————————————————

Crop Sequence

1.3 (0.11) 1.4 (0.07) 1.5 (0.11) 1.2 (0.05) 1.2 (0.05) 1.4 (0.07) 1.5 (0.03) 0.004

1.7 (0.11) 1.5 (0.13) 1.5 (0.1)

1.4 (0.16) 1.4 (0.07) 1.5 (0.12) 1.4 (0.09) 1.7 (0.1)

1.5 (0.1)

Cu

13.2 (0.15) 13.6 (0.35) 13.4 (0.09) 13.3 (0.20) 13.5 (0.13) 13.3 (0.29) 13.4 (0.18) 13.2 (0.22) 13.5 (0.15) 13.7 (0.07) 13.4 (0.28) 13.4 (0.20) 13.3 (0.19) 13.7 (0.12) 13.6 (0.15) 14.0 (0.51) 0.59

C:N

0.30

6.5 (0.3)

6.6 (0.1)

6.8 (0.1)

6.9 (0.1)

6.5 (0.2)

6.5 (0.1)

6.9 (0.3)

6.2 (0.1)

6.6 (0.3)

6.0 (0.2)

6.1 (0.3)

6.3 (0.1)

6.6 (0.3)

6.8 (0.2)

6.7 (0.4)

6.4 (0.2)

pH

0.19 (0.008) 0.17 (0.016) 0.18 (0.004) 0.18 (0.006) 0.19 (0.005) 0.20 (0.01) 0.20 (0.013) 0.18 (0.008) 0.19 (0.003) 0.18 (0.015) 0.19 (0.009) 0.18 (0.014) 0.17 (0.009) 0.18 (0.004) 0.19 (0.013) 0.20 (0.006) 0.34

2.8 (0.17) 0.40

2.2 (0.12) 2.5 (0.07) 2.6 (0.2)

2.5 (0.13) 2.3 (0.22) 2.4 (0.07) 2.5 (0.11) 2.6 (0.08) 2.7 (0.20) 2.7 (0.20) 2.4 (0.14) 2.6 (0.05) 2.5 (0.21) 2.5 (0.17) 2.4 (0.2)

5.5 (0.09) 5.4 (0.2) 5.6 (0.1) 5.8 (0.4) 5.8 (0.4) 5.3 (0.3) 5.6 (0.03) 5.5 (0.5) 5.6 (0.3) 5.4 (0.4) 5.1 (0.2) 5.4 (0.2) 5.7 (0.4) 5.8 (0.2) 0.64

5.4 (0.2) 5 (0.5)

Total N TOC OM —————————————————————%——————————————————

Table 2 Soil properties in spring 2016. Mean values for soil properties are given along with standard errors of the mean in parentheses for each crop sequence. P-values associated with the Crop Sequence term in the linear model, Yield ∼ Block + Crop Sequence are given.

N. Strom, et al.

Applied Soil Ecology xxx (xxxx) xxxx

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

Fig. 2. Non-metric multidimensional scaling plot of fungal communities at the OTU level (A) and a distance-based redundancy analysis of fungal communities at the ordinal level (B) in midseason 2016. Crop sequence symbols in (A) are positioned on the centroids of fungal communities associated with each crop sequence. Orders in (B) are positioned based on their scores along each ordination axis. Several orders that mapped near the center of the plot or that overlapped with other orders were removed to facilitate display. Fig. 3. Nematophagous fungi and the SCN. Relative abundance of the nematode-trapping fungal guild (A) and the nematode egg-parasitic fungus Clonostachys (C) and the SCN across crop sequences (data pooled across all time points). Relative abundance of the nematode-trapping fungal guild (B) and Clonostachys (D) by SCN density (data from spring 2016). Relative abundances of fungi are centered log ratio transformed, and SCN density is cube root transformed.

Nematode-trapping fungi displayed a soybean-associated pattern of relative abundance similar to that of SCN egg density (Fig. 3A), with the highest relative abundance associated with later years of soybean monoculture, including both SCN-susceptible (Ss) and SCN-resistant (Sr) soybean plots. The relative abundance of nematode-trapping fungi was significantly (p < 0.05) correlated with SCN density only in spring 2016 (Fig. 3B) and spring 2015 (Fig. S6A). At the species-level, nematode-trapping fungi that displayed a soybean-associated pattern included Orbilia auricolor and several species of Arthrobotrys (Fig. S7), two of which were also significantly positively correlated with SCN

density (Fig. S8). Although the nematode egg parasite guild as a whole did not show a pattern of increasing relative abundance under soybean (Fig. S6B), selected nematode egg parasitic taxa, most notably Clonostachys, displayed a soybean-associated pattern (Fig. 3C). Clonostachys also showed significant correlations (p < 0.05) with SCN density at four out of six sampling time points (Figs. 3D and S6A). Other potential egg parasitic taxa exhibiting this pattern included Fusarium solani (Chen and Chen, 2003), several Mortierella species (Juba et al., 2004), Didymella americana (syn. Peyronellaea americana) (Aveskamp et al., 2010), and the 7

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

basidiomycete, Coprinellus micaceus (syn. Coprinus micaceus) (Degenkolb and Vilcinskas, 2016; Plaza et al., 2016) (Figs. S7 and S8). The well-characterized nematode egg parasite P. lilacinum (Song et al., 2016) was below the limits of detection in many corn monoculture plots but was consistently detected in long-term susceptible soybean monoculture plots (Ss) (data not shown). Nematode endoparasites did not show a consistent pattern across crop sequences (Fig. S6B). Soybean-associated taxa also included several potential soybean pathogens, such as Septoria arundinacea, Fusicolla merismoides (Syn. Fusarium merismoides), and Dactylonectria macrodidyma (Malapi-Wight et al., 2015) (Fig. S7). Several fungi that have been shown to be capable of colonizing both soybean roots and SCN cysts also exhibited a soybean-associated pattern. These included Paraphoma radicina (Stiles and Glawe, 1989) and the soybean pathogens Corynespora cassiicola (Carris et al., 1986), Cadophora gregata (Syn. Phialophora gregata) (Carris et al., 1986), and F. solani (Stiles and Glawe, 1989) (Fig. S8). Taken as a group, soybean pathogens exhibited a soybean-associated pattern at all time points (Figs. 4A and S9), but neither this group nor any individual fungal taxa were correlated either positively or negatively with soybean yield (Figs. 4B, S8, and S10). Similarly, increasing years of corn monoculture saw greater relative abundance of potential corn pathogens in the genera Setophoma (Lević et al., 2013), Drechslera (Sugawara et al., 1987), Cladosporium (Robertson et al., 2011), Plenodomus, Ceratocystis, Parastagonospora, Phaeosphaeria, and Phaeosphaeriopsis (Marin-Felix et al., 2017; Quaedvlieg et al., 2013), and Nigrospora (Standen, 1945) (Fig. S7). Taken as a group, these fungi showed a corn-associated pattern at all time points (Figs. 4C and S9) and significant (p < 0.05) negative correlations with corn yield in four out of six sampling time points (Figs. 4D and S10). Individual taxa that were negatively correlated with corn yield included Plenodomus biglobosus, Parastagonospora nodorum, and Nigrospora oryzae (Fig. S8). Several fungi that are not known corn pathogens also increased with increasing years of corn, but only two taxa, Exophiala sp. and Articulospora sp., were negatively correlated with corn yield at a single time point in Spring 2016 (Fig. S8). Several potentially beneficial fungi also showed correlations with crop monoculture and yield. The phylum Glomeromycota, which contains all AMF, became more abundant with increasing years of corn monoculture (Figs. 4E and S9) and was significantly (p < 0.05) negatively correlated with corn yield in spring 2016 (Fig. 4F) and fall 2016 (Fig. S10). At the species-level, a corn-associated pattern was observed for AMF identified as Paraglomus brasilianum and Glomus indicum, as well as for two dark septate endophytic taxa, Periconia macrospinosa (Doran et al., 1984) and an OTU identified to the family Serendipitaceae in the order Sebacinales (Fig. S7). By contrast, only one AMF species, Rhizophagus irregularis, showed a soybean-associated pattern in midseason of both 2015 and 2016 (Fig. S7). Mortierella sp. had significantly greater abundance under soybean monoculture in spring 2016 (Fig. 4G) and midseason 2015 (Fig. S9) and correlated positively with corn yield in spring 2016 (Fig. 4H).

Contrary to our predictions, links in the model between the SCN and nematophagous fungi were not significant and were removed during model reduction (Fig. 5B, Supplementary Methods). Instead, soil pH was a significant determinant of SCN density, while continuous soybean monoculture had a strong positive direct effect on the abundance of nematophagous fungi (Fig. 5C, Table 3). In contrast to host-specific fungal pathogens, which had a significant link to yield in the corn model, neither of the potential mutualistic fungi (AMF, Mortierella) had direct positive or negative effects on yield in the reduced model for either crop (Fig. 5). No significant (p < 0.05) links were identified between soil P and Mortierella or AMF, but P did explain some variation in AMF abundance under corn monoculture. Despite a significant shift in soil P observed across crop sequences (Table 2), this nutrient did not significantly affect yield in the reduced models for either crop (Fig. 5). Continuous monoculture had a direct effect on levels of soil P for both crops,and soil pH significantly explained some variation in P for corn and AMF for soybean (Fig. 5, Table 3). 5. Discussion 5.1. Fungal communities shaped by long-term monoculture Our finding that soil fungal communities become more similar to those of their respective long-term monoculture plots over consecutive years of monoculture (Fig. 2A) is in agreement with other studies showing this same pattern for coffee (Zhao et al., 2018), vanilla (Xiong et al., 2014), the medicinal herb Pseudostellaria heterophylla (Wu et al., 2016), and soybean (Bai et al., 2015). The distance-based redundancy analysis (Fig. 2B) suggests that these shifts in bulk soil communities are explained in part by shifts in soil properties that favor the proliferation of certain groups of fungi, as has been observed, for example, by Lauber et al. (2008), who found that soil pH and soil nutrients were stronger predictors of bacterial and fungal community structure than land use history. However, given that a model including a crop sequence term explained more community variation than a model with soil properties, alone, it is likely that other factors, for example the rhizosphere effect (Hiltner, 1904), contributed to gradual shifts in soil fungal communities over time (Kristin and Miranda, 2013; Lapsansky et al., 2016; Philippot et al., 2013). In our study system, it is also likely that the SCN played a role in shaping fungal communities under long-term soybean monoculture. 5.2. Nematophagous fungi increase in abundance and diversity under soybean monoculture The hypothesis that nematophagous fungi would increase in abundance and diversity under continuous soybean monoculture was supported for the nematode-trapping fungal guild and for some nematode egg parasites (Fig. 3). Trapping fungi that produce adhesive knobs (e.g. Dactylellina ellipsospora) or constricting rings (e.g. Arthrobotrys dactyloides) have been shown to respond to an increase in nematodes as a food source more than trapping fungi that produce adhesive networks (e.g. Arthrobotrys oligospora) (Jaffee, 2003), potentially due to the greater saprophytic ability of adhesive network-forming species (Persmark, 1996). It was, therefore, surprising that, of the trapping fungi we detected, only the adhesive network-forming taxa Arthrobotrys polycephala (Yu et al., 2014) and Arthrobotrys xiangyunensis (Liu et al., 2014) showed significant positive correlations with SCN egg density (Fig. S8). Other possible explanations for the increased abundance and diversity of trapping fungi observed over long-term soybean monoculture include colonization of the plant host as endophytes or rhizosphere associates (Bordallo et al., 2002) or predation on nematodes other than SCN. In addition to the SCN, bacteria-feeding nematodes have been shown to proliferate under continuous soybean monoculture at our study site (Grabau and Chen, 2016c), and it is possible that the

4.7. Structural equation modeling Starting from initial full models based on a conceptual meta-model (Fig. 5A), we performed model reduction though a process of removing individual links and performing likelihood ratio tests of changes in model fit (χ2) and permanently removing those links that did not significantly impact the model fit (Supplementary Methods). Final reduced good-fitting models were identified separately for each crop (Fig. 5, Table 3). The final reduced models revealed that fungal corn pathogens and levels of soil N were the most important measured variables explaining corn yield (Fig. 5B, Table 3). However, some variation in corn yield could not be explained by any of the variables in this study and was explained instead by a direct link between corn monoculture and yield in this model (Fig. 5B, Table 3). The SCN and soil N were the most important variables explaining soybean yield (Fig. 5C, Table 3). 8

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

Fig. 4. Differential abundance across crop sequences and correlations with crop yield for fungal soybean pathogens (A, B), fungal corn pathogens pathogens (C, D), arbuscular mycorrhizal fungi (E, F), and the phosphate-solubilizing fungal genus, Mortierella (G, H) All data is from spring 2016. Relative abundances of fungi were centered log ratio transformed. Statistical significance is shown according to ANOVA for differences in relative abundance across crop sequences.

9

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

Fig. 5. The conceptual meta-model (A) used for structural equation modeling designed to measure the effect of continuous monoculture on key biotic (pathogens and fungal mutualists) and abiotic (soil properties) factors and to test hypotheses (H1-H3) regarding the effect of these factors on crop yield. Reduced structural equation models for the effects of corn (B) and soybean (C) monoculture on crop yield. In final reduced models, line thickness is proportional to each SEM-estimated regression coefficient. Solid lines indicate positive relationships, and dashed lines indicate negative relationships. Non-significant links (p > 0.05) are displayed in grey. Soil properties data is from spring 2016. Fungal community data and SCN data is from midseason 2016.

relative abundance and diversity of trapping fungi increased under soybean monoculture in response to the population density of these nematodes rather than in response to SCN populations.

While some egg parasitic taxa showed a similar increase in relative abundance under soybean monoculture, the lack of a strong pattern for the egg-parasitic guild overall may be due to the fact that many

Table 3 Raw and standardized estimates of reduced structural equation models in Fig. 5. Crop Corn Regressions

Covariances Soybean Regressions

Parameter

Estimate

SE

z-value

p-value

Standardized estimate

Monoculture to Mortierella N to Mortierella pH to Mortierella pH to AMF P to AMF Monoculture to Corn Pathogens P to Corn Pathogens Monoculture to pH Monoculture to P pH to P N to Yield Corn Pathogens to Yield Monoculture to Yield Mortierella and Glomeromycota Glomeromycota and Corn Pathogens

−0.182 0.161 −0.237 −0.281 −0.296 0.302 −0.350 −0.246 −0.611 −0.546 0.485 −0.396 −0.394 −0.224 0.273

0.086 0.077 0.091 0.201 0.190 0.184 0.192 0.198 0.148 0.148 0.147 0.155 0.164 0.090 0.144

−2.115 2.086 −2.597 −1.402 −1.559 1.643 −1.821 −1.242 −4.134 −3.693 3.299 −2.556 −2.401 −2.476 1.890

0.034 0.037 0.009 0.161 0.119 0.100 0.069 0.214 < 0.001 < 0.001 0.001 0.011 0.016 0.013 0.059

−0.351 0.310 −0.455 −0.274 −0.289 0.300 −0.347 −0.246 −0.611 −0.546 0.496 −0.408 −0.403 −0.549 0.349

Monoculture to pH to P N to pH Monoculture to pH to SCN Monoculture to Monoculture to pH to AMF N to AMF N to Yield SCN to Yield

0.617 −0.292 −0.307 0.263 0.379 0.641 −0.322 −0.44 −0.247 0.099 −0.137

0.158 0.154 0.194 0.176 0.176 0.156 0.172 0.169 0.170 0.045 0.045

3.904 −1.898 −1.579 1.494 2.152 4.109 −1.878 −2.611 −1.452 2.215 −3.026

< 0.001 0.058 0.114 0.135 0.031 < 0.001 0.060 0.009 0.147 0.027 0.002

0.602 −0.285 −0.307 0.268 0.385 0.642 −0.329 −0.450 −0.253 0.350 −0.478

P SCN Nematophagous Fungi AMF

10

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

nematode egg-parasitic taxa occupy multiple ecological niches in the agroecosystem and do not depend exclusively on the nematode host for nutrition. For example, isolates of F. solani can act both as plant pathogens (Rupe et al., 1997) and soil saprotrophs (O’Donnell et al., 2008), while some isolates have been shown to parasitize SCN eggs (Chen and Chen, 2003). As with trapping fungi, it is possible that the relative abundance of some nematode egg parasites was greater under long-term soybean monoculture because the plant host, rather than the SCN, hosts these fungi as endophytes. Nonetheless, it is intriguing that many fungi that have been isolated directly from SCN cysts, especially Clonostachys, showed positive correlations with SCN density, suggesting that the SCN was a factor in the proliferation of some fungi in this guild.

on N fertilizer (Cooper, 2007; Salvagiotti et al., 2008). However, N fertilization has been shown to improve soybean yield even when rhizobia are present (Salvagiotti et al., 2008). Furthermore, it may be possible that populations of rhizobia evolve to become less mutualistic over continuous soybean monoculture or evolve towards more saprophytic lifestyles in the continued absence of a legume host (Kiers et al., 2002), as under long term corn monoculture. Levels of soil nutrients did not fully explain monoculture yield decline in either crop, and structural equation models showed that biotic factors also played a role (Fig. 5). Continuous monoculture has been shown to lead to a proliferation of both harmful and beneficial soil microbes that affect crop yield (Liu and Herbert, 2002; Wang et al., 2012). It was not surprising that fungal pathogens of corn had a direct negative effect on corn yield, as these have been identified as yieldlimiting factors in other studies (Jirak-Peterson and Esker, 2011; Kozhevnikova, 1975). However, it is important to note that the plants in our study system did not appear diseased, and it is possible that the taxa identified as corn pathogens that were negatively correlated with corn yields affected root health without causing overt aboveground symptoms (Cook, 1984). By contrast, host-specific fungal pathogens of soybean did not significantly affect soybean yield (Fig. 5), which is in line with a previous study at our site (Whiting and Crookston, 1993) that found that the prevalence and severity of brown stem rot caused by C. gregata, while it increased over continuous soybean monoculture, did not affect soybean yield. Instead, the structural equation model identified SCN as the most important biotic factor affecting soybean yield (Fig. 5, Table 3). While pH did not directly affect yield of either crop, the positive relationship between soil pH and SCN density (Fig. 5, Table 3) suggests that the negative effect of higher soil pH on soybean yield may be mediated through its effect on SCN density. A similar conclusion was reached in a previous study on soil pH, SCN, and soybean yield conducted in Iowa and Wisconsin (Pedersen et al., 2010). Thus, our findings regarding yield decline under corn and soybean monoculture are consistent with those from previous culture-based studies, suggesting that metabarcoding approaches are useful for analyzing biotic factors associated with yield declines. Beneficial microbes that may accumulate under monoculture include potential symbionts such as AMF and other fungi that aid in nutrient acquisition or provide protection from abiotic or biotic stresses. Given that both AMF (Miller, 2000) and Mortierella (Li et al., 2018) have been cited as increasing corn yields, it was surprising that they did not directly or indirectly influence crop yield in the final structural equation models of either crop (Fig. 5). Previous studies on the cereal cyst nematode (Westphal and Becker, 1999) and the SCN (Hu et al., 2017) have shown that soil fumigation results in increased populations of plant-parasitic nematodes, presumably resulting from the loss of nematophagous bacteria and fungi that preyed on or parasitized these plant pathogens. Although we show that increases in the abundance of nematophagous fungi paralleled those of the SCN over continuous soybean monoculture (Fig. 3), nematophagous fungi and the SCN did not directly affect one another in the structural equation model (Fig. 5), suggesting that nematophagous fungi proliferated under continuous soybean monoculture independent of SCN density in our study system. It is important to note that while the structural equation model identified soil N and crop host-specific plant pathogens as the strongest measured predictors for yield, there are unidentified variables affecting corn yield that were captured by the direct link between crop monoculture and corn yield. Furthermore, a significant portion of variation in both corn and soybean yield was left unexplained, suggesting that there are other factors involved in corn and soybean monoculture yield decline that were not measured in this study. Detrimental rhizosphere microorganisms, especially rhizobacteria that decrease plant growth, are hypothesized to play an important role in monoculture yield decline. Fumigation of soils from long-term corn monoculture plots has been shown to improve yields comparable to those observed in rotation

5.3. Relationships between soil P and fungal communities No phosphorus-containing fertilizer was applied to the plots in this study, so other explanations for differences in P levels across crop sequences are needed. The common soil and rhizosphere fungus, Mortierella, has been shown to increase the amount of bioavailable P in a variety of soil types (Osorio and Habte, 2014). The positive correspondence between Mortierellales and soil P (Fig. 2B) may be a result of these fungi converting inorganic or organic phosphates into the bioavailable form detected in our assay and, given that Mortierella was more abundant under soybean (Fig. 4G), may partially explain the greater levels of soil P observed under soybean monoculture (Table 2). By contrast, the decline in soil P under corn monoculture may have led to the associated increase in relative abundance of AMF, which respond negatively to higher levels of soil P (Liu et al., 2012). Abundant AMF under corn monoculture may have also played a role in depleting P from the soil (Zhang et al., 2010). OTUs in the order Sebacinales also increased under corn monoculture and corresponded with decreased soil P. One well-studied member of Sebacinales, Serendipita indica (syn. Piriformospora indica), is associated with increased plant levels of N, K, and P (Kumar et al., 2012) and has been shown to actively transport P to corn roots (Yadav et al., 2010). Collectively, these findings suggest that the corn-associated fungal community may help corn to adapt to a low-P environment but may also contribute to the depletion of soil P under continuous corn monoculture. However, structural equation modeling offered little support for relationships between fungi and soil P. Instead, continuous monoculture of both crops had a direct negative and positive effect on soil P in corn and soybean, respectively, and soil pH was a significant determinant of soil P, AMF, and Mortierella under one or the other crop (Fig. 5, Table 3). Soil P becomes increasingly fixed by soil minerals below pH 6 and is typically most available to plants when the soil pH is between 6 and 7 (Hyland et al., 2005). It was surprising that pH was negatively related to soil P in the structural equation model. However, pH affects the adsorption of P by various soil constituents in different ways (Devau et al., 2009), and it is possible that some soil constituents in our system adsorbed more P at a higher pH, making soil P less bioavailable. 5.4. Interactions among abiotic and biotic factors and contributions to monoculture yield decline Many factors, both abiotic and biotic, have been hypothesized to play a role in monoculture yield decline. The depletion of key soil nutrients under continuous cropping with conventional tillage is a wellstudied phenomenon (Edwards et al., 1992; Karlen et al., 2006; Riedell et al., 1998), with soil N identified as a major factor limiting the yield of corn in continuous monoculture (Gentry et al., 2013). Therefore, it was not surprising that the structural equation model showed that higher levels of soil N were correlated with improved yields of corn (Fig. 5, Table 3). However, it was somewhat surprising to find that soil N also contributed to higher soybean yields. As a legume, soybean forms symbioses with nitrogen-fixing rhizobia that make this plant less reliant 11

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

plots, an effect that has been hypothesized to be due to removal of deleterious, but non-pathogenic, rhizosphere bacteria that accumulate under continuous corn monoculture (Turco et al., 1990). Deleterious rhizosphere bacteria have also been implicated in yield reductions in continuous monocultures of barley (Alström, 1992; Olsson and Alström, 1996) and potato (Bakker and Schippers, 1987). Mechanisms of growth inhibition may involve production of phytotoxins (Barazani et al., 1999), but deleterious rhizosphere bacteria may also compete with the plant for nutrients (Nehl et al., 1996) or inhibit mycorrhizal colonization (Krishna et al., 1982). Similarly, some non-pathogenic saprobic fungi have been implicated in monoculture yield declines in sugar cane (Pankhurst et al., 2003). Johnson et al (1992) also hypothesized that AMF may become detrimental or less beneficial under monoculture. Our study did not find strong evidence that either detrimental nonpathogenic fungi or AMF contributed to yield increases or declines in corn or soybean. Only two non-pathogenic fungi, Exophiala, a soil saprobe and root endophyte (Cheikh-Ali et al., 2015), and Articulospora, an aquatic fungus that can also grow as an endophyte in submerged roots (Bärlocher, 2007), were negatively correlated with corn yields at a single time point (Fig. S8). AMF showed negative correlations with corn yield at two time points (Figs. 4F and S10), but did not directly impact yield in the structural equation models (Fig. 5). Nonetheless, additional biotic factors such as rhizosphere bacteria as well as abiotic factors like soil structure (Karlen et al., 2006) and chemically mediated processes such as autotoxicity (Nickel et al., 1995; Singh et al., 1999) may have contributed to monoculture yield decline in this system and should be addressed in future studies.

processed Illumina metabarcoding data, developed bioinformatics and statistical approaches, analyzed the data, and wrote the manuscript. KEB co-conceived of, supervised and provided guidance on the research, and edited the manuscript. WH helped collect cysts and soil samples, provided training and analytical pipelines for analyzing the data, and edited the manuscript. DH helped collect cysts and soil samples and edited the manuscript. SC co-conceived of and supervised the project, maintained the long-term research study plot site, and provided expertise on SCN biology. Funding This research was supported by United States Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA) grant 2015-67013-23419. Declaration of Competing Interest The submitted work was carried out in the absence of any personal, professional or financial relationships that could potentially be construed as a conflict of interest. Acknowledgements Fungal cultures and tissue for mock communities were generously supplied by Todd Burnes, Peter Kennedy, Senyu Chen, Harold Corby Kistler, and Timothy James. The authors would like to thank Wayne Gottschalk, Cathryn Johnson, Jeff Bauman, and undergraduate workers for their help in collecting soil samples and Bryani Lee for her assistance in processing soil samples and extracting DNA. We also thank Nicholas Dunn at the Minnesota Supercomputing Institute and Jon Palmer for help with implementing AMPtk, and Gabriel Al-Ghalith for help with statistical analysis using centered log ratios. We would also like to acknowledge Daryl Gohl, Allison MacLean, and Corbin Dirkx at the University of Minnesota Genomics Center for sharing their expertise and methods development in amplification and sequencing of fungal DNA for metabarcoding experiments. We thank Eric Seabloom and Elizabeth Borer for their advice and assistance with structural equation modeling.

6. Conclusions This study examined how corn and soybean monoculture alters fungal communities, soil properties, and SCN density and provides insights into the contribution of these three factors and interactions amongst them to monoculture yield decline. We showed that continuous corn and soybean monoculture was associated with significant changes in bulk soil fungal communities and the proliferation of specific functional groups, with host-specific fungal pathogens increasing in abundance under both crops, AMF proliferating under corn, and nematophagous fungi and phosphate-solubilizing fungi (Mortierella) proliferating under soybean. The relative abundance of nematode-trapping fungi and some nematode egg parasitic taxa tracked SCN density, suggesting that some members of these guilds, especially Clonostachys, are potential predators or parasites of the SCN and should be investigated for their potential to control SCN populations. However, structural equation modeling did not identify significant interactions between the SCN and nematophagous fungi as a whole but instead supported a direct link between soybean monoculture and nematophagous fungi, suggesting that factors other than the SCN may also drive abundance of nematophagous fungi. A positive correspondence observed between the relative abundance of Mortierella and soil P and a negative correspondence between AMF and soil P suggested biological causes for the large shifts in soil P and AMF across crop sequences observed in our study system. Structural equation modeling, however, supported a stronger role for soil pH in affecting both soil P levels and the abundance of AMF, Mortierella, and the SCN in this system. The availability of soil N and the relative abundance of host-specific pathogens were identified as the most important factors affecting yields of corn and soybean at this study site. These findings illustrate the complexity of relationships between crop sequences, soil properties, soil fungal communities, and plant parasitic nematodes and provide fundamental knowledge that can guide management strategies to control the SCN and improve corn and soybean yields.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.apsoil.2019.103388. References Aitchison, J., 1982. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 139–177. Alström, S., 1992. Saprophytic soil microflora in relation to yield reductions in soil repeatedly cropped with barley (Hordeum vulgare L.). Biol. Fertil. Soils 14, 145–150. Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. Aveskamp, M.M., de Gruyter, J., Woudenberg, J.H.C., Verkley, G.J.M., Crous, P.W., 2010. Highlights of the Didymellaceae: a polyphasic approach to characterise Phoma and related pleosporalean genera. Stud. Mycol. 65, 1–60. Bai, L., Cui, J., Jie, W., Cai, B., 2015. Analysis of the community compositions of rhizosphere fungi in soybeans continuous cropping fields. Microbiol. Res. 180, 49–56. Bakker, A.W., Schippers, B., 1987. Microbial cyanide production in the rhizosphere in relation to potato yield reduction and Pseudomonas spp-mediated plant growth-stimulation. Soil Biol. Biochem. 19, 451–457. Barazani, O., Friedman, J., Aviv, T., 1999. Allelopathic bacteria and their impact on higher plants. Crit. Rev. Plant Sci. 18, 741–755. Bärlocher, F., 2007. Fungal endophytes in submerged roots. In: Schulz, B., Boyle, C., Sieber, T. (Eds.), Microbial Root Endophytes. Springer-Verlag, Berlin Heidelberg, pp. 179–190. Bender, R.R., Haegele, J.W., Below, F.E., 2015. Nutrient uptake, partitioning, and remobilization in modern soybean varieties. Agron. J. 107, 563–573. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300. Bever, J.D., 2002. Negative feedback within a mutualism: host-specific growth of

Author contributions NS assisted in collection of soil samples and DNA isolations, 12

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al. mycorrhizal fungi reduces plant benefit. Proc. R. Soc. B Biol. Sci. 269, 2595–2601. Bever, J.D., Westover, K.M., Antonovics, J., 1997. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J. Ecol. 85, 561–573. Bordallo, J.J., Lopez-Llorca, L.V., Jansson, H.B., Salinas, J., Persmark, L., Asensio, L., 2002. Colonization of plant roots by egg-parasitic and nematode-trapping fungi. New Phytol. 154, 491–499. Borer, E.T., Seabloom, E.W., Tilman, D., 2012. Plant diversity controls arthropod biomass and temporal stability. Ecol. Lett. 15, 1457–1464. Bray, R., Kurtz, L.T., 1945. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 59, 39–46. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Peña, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., Mcdonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Publ. Gr. 7, 335–336. Carris, L.M., Glawe, D.A., Gray, L.E., 1986. Isolation of the soybean pathogens Corynespora cassiicola and Phialophora gregata from cysts of Heterodera glycines in Illinois. Mycologia 78, 503–506. Cheikh-Ali, Z., Glynou, K., Ali, T., Ploch, S., Kaiser, M., Thines, M., Bode, H.B., MaciáVicente, J.G., 2015. Diversity of exophillic acid derivatives in strains of an endophytic Exophiala sp. Phytochemistry 118, 83–93. Chen, S., Chen, F., 2003. Fungal parasitism of Heterodera glycines eggs as influenced by egg age and pre-colonization of cysts by other fungi. J. Nematol. 35, 271–277. Chen, S., Dickson, D.W., 2012. Biological control of plant-parasitic nematodes. In: Manzanilla-López, R.H., Marbán-Mendoza, N. (Eds.), Practical Plant Nematology. Colegio de Postgraduados and Mundi-Prensa, Biblioteca Básica de Agricultura, Guadalajara, Jalisco, Mexico, pp. 761–811. Cook, R.J., 1984. Root health: importance and relationship to farming practices. In: Bezdicek, D.F. (Ed.), Organic Farming: Current Technology and Its Role in a Sustainable Agriculture. ASA Spec. Publ., Madison, Wisconsin, USA, pp. 111–127. Cooper, J.E., 2007. Early interactions between legumes and rhizobia: disclosing complexity in a molecular dialogue. J. Appl. Microbiol. 103, 1355–1365. Copeland, P.J., Crookston, R.K., 1992. Crop sequence affects nutrient composition of corn and soybean grown under high fertility. Agron. J. 84, 503–509. Copeland, P.J., Allmaras, R.R., Crookston, R.K., Nelson, W.W., 1993. Corn-soybean rotation effects on soil water depletion. Agron. J. 85, 203. Crookston, R.K., Kurle, J.E., 1989. Corn residue effect on the yield of corn and soybean grown in rotation. Agron. J. 81, 229. Crookston, K.R., Kurle, J.E., Lueschen, E., 1988. Relative ability of soybean, fallow, and triacontanol to alleviate yield reductions associated with growing corn continously. Crop Sci. 28, 145–147. Crookston, R.K., Kurle, J.E., Copeland, P.J., Ford, J.H., Lueschen, W.E., 1991. Rotational cropping sequence affects yield of corn and soybean. Agron. J. 83, 108–113. Degenkolb, T., Vilcinskas, A., 2016. Metabolites from nematophagous fungi and nematicidal natural products from fungi as alternatives for biological control. Part I: metabolites from nematophagous ascomycetes. Appl. Microbiol. Biotechnol. 100, 3799–3812. Devau, N., Cadre, E.L., Hinsinger, P., Jaillard, B., Gérard, F., 2009. Soil pH controls the environmental availability of phosphorus: experimental and mechanistic modelling approaches. Appl. Geochem. 24, 2163–2174. Doran, J.W., Wilhelm, W.W., Power, J.F., 1984. Crop residue removal and soil productivity with no-till corn, sorghum, and soybean. Soil Sci. Soc. Am. J. 48, 640–645. Edwards, J.H., Wood, C.W., Thurlow, D.L., Ruf, M.E., 1992. Tillage and crop rotation effects on fertility status of a Hapludult soil. Soil Sci. Soc. Am. J. 56, 1577–1582. Faghihi, J., Ferris, J.M., 2000. An efficient new device to release eggs from Heterodera glycines. J. Nematol. 32, 411–413. Frøslev, T.G., Kjøller, R., Bruun, H.H., Ejrnæs, R., Brunbjerg, A.K., Pietroni, C., Hansen, A.J., 2017. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8, 1–11. Garbe, J.R., 2015. Gopher-biotools. (Version 1.0) [Computer program]. Available at. https://bitbucket.org/jgarbe/gopher-biotools. Gentry, L.F., Ruffo, M.L., Below, F.E., 2013. Identifying factors controlling the continuous corn yield penalty. Agron. J. 105, 295–303. Gloor, G.B., 2018. ALDEx2: ANOVA-Like Differential Expression Tool for Compositional Data [WWW Document]. URL https://github.com/ggloor/ALDEx_bioc/blob/ master/vignettes/ALDEx2_vignette.pdf (Accessed 5 July 2018). . Gloor, G.B., Macklaim, J.M., Pawlowsky-Glahn, V., Egozcue, J.J., 2017. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 1–6. Gohl, D.M., Vangay, P., Garbe, J., MacLean, A., Hauge, A., Becker, A., Gould, T.J., Clayton, J.B., Johnson, T.J., Hunter, R., Knights, D., Beckman, K.B., 2016. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–948. Grabau, Z.J., Chen, S., 2016a. Determining the role of plant-parasitic nematodes in the corn–soybean crop rotation yield effect using nematicide application: I. Corn. Agron. J. 108, 782–793. Grabau, Z.J., Chen, S., 2016b. Determining the role of plant-parasitic nematodes in the corn-soybean crop rotation yield effect using nematicide application: II. Soybean. Agron. J. 108, 1168–1179. Grabau, Z.J., Chen, S., 2016c. Influence of long-term corn-soybean crop sequences on soil ecology as indicated by the nematode community. Appl. Soil Ecol. 100, 172–185. Gracia-Garza, J.A., Neumann, S., Vyn, T.J., Boland, G.J., 2002. Disease control Influence of crop rotation and tillage on production of apothecia by Sclerotinia sclerotiorum. Can. J. Plant Pathol. 143, 137–143. Hiltner, L., 1904. Über neuere erfahrungen und probleme auf dem gebiete der

bodenbakteriologie unter besonderer berücksichtigung der gründüngung und brache. Arb. der Dtsch. Landwirtsch. Gesellschaft 98, 59–78. Hu, W., Samac, D.A., Liu, X., Chen, S., 2017. Microbial communities in the cysts of soybean cyst nematode affected by tillage and biocide in a suppressive soil. Appl. Soil Ecol. 119, 396–406. Hu, W., Strom, N., Haarith, D., Chen, S., Bushley, K.E., 2018. Mycobiome of cysts of the soybean cyst nematode under long term crop rotation. Front. Microbiol. 9, 1–19. Hyland, C., Ketterings, Q., Dewing, D., Stockin, K., Czymmek, K., Albrecht, G., Geohring, L., 2005. Phosphorus Basics – The Phosphorus Cycle [WWW Document]. Cornell Univ. Coop. Ext. URL http://nmsp.cals.cornell.edu/publications/factsheets/factsheet12.pdf (Accessed 17 September 2019). Jaffee, B., 2003. Correlations between most probable number and activity of nematodetrapping fungi. Phytopathology 93, 1599–1605. Jirak-Peterson, J.C., Esker, P.D., 2011. Tillage, crop rotation, and hybrid effects on residue and corn anthracnose occurrence in Wisconsin. Plant Dis. 95, 601–610. Johnson, N., Copeland, P., Crookston, R., Pfleger, F., 1992. Mycorrhizae: possible explanation for yield decline with continuous corn and soybean. Agron. J. 84, 387–390. Johnson, N., Pfleger, F.L., Crookston, R.K., Simmons, S.R., Copeland, P.J., 1991. Vesicular–arbuscular mycorrhizas respond to corn and soybean cropping history. New Phytol. 117, 657–663. Juba, J., Meyer, S., Humber, R., Liu, X.Z., Nitao, J., Huettel, R., 2004. Activity of fungal culture filtrates against soybean cyst nematode and root-knot nematode egg hatch and juvenile motility. Nematology 6, 23–32. Karlen, D.L., Hurley, E.G., Andrews, S.S., Cambardella, C.A., Meek, D.W., Duffy, M.D., Mallarino, A.P., 2006. Crop rotation effects on soil quality at three northern corn/ soybean belt locations. Agron. J. 98, 484–495. Kerry, B., 1988. Fungal parasites of cyst nematodes. Agric. Ecosyst. Environ. 24, 293–305. Kerry, B.R., Crump, D.H., 1998. The dynamics of the decline of the cereal cyst nematode, Heterodera avenae, in four soils under intensive cereal production. Fundam. Appl. Nematol. 21, 617–625. Kiers, E.T., West, S.A., Denison, R.F., 2002. Mediating mutualisms: farm management practices and evolutionary changes in symbiont co-operation. J. Appl. Ecol. 39, 745–754. Kozhevnikova, L.M., 1975. Infection of maize by blister and loose smuts under condition of continuous sowing and the measures decreasing damage by the disease. Ref. Zhurnal 9, 167–175. Krishna, K.R., Balakrishna, A.N., Bagyaraj, D.J., 1982. Interaction between a vesiculararbuscular mycorrhizal fungus and Streptomyces cinnamomeous and their effects on finger millet. New Phytol. 92, 401–405. Kristin, A., Miranda, H., 2013. The root microbiota-a fingerprint in the soil? Plant Soil 370, 671–686. Kruskal, J.B., 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1–27. Kruskal, W.H., Wallis, W.A., 1952. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621. Kumar, V., Sarma, M.V.R.K., Saharan, K., Srivastava, R., Kumar, L., Sahai, V., Bisaria, V.S., Sharma, A.K., 2012. Effect of formulated root endophytic fungus Piriformospora indica and plant growth promoting rhizobacteria fluorescent pseudomonads R62 and R81 on Vigna mungo. World J. Microbiol. Biotechnol. 28, 595–603. Lapsansky, E.R., Milroy, A.M., Andales, M.J., Vivanco, J.M., 2016. Soil memory as a potential mechanism for encouraging sustainable plant health and productivity. Curr. Opin. Biotechnol. 38, 137–142. Lauber, C.L., Strickland, M.S., Bradford, M.A., 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. Lee, S.C., Tang, M.S., Lim, Y.A.L., Choy, S.H., Kurtz, Z.D., Cox, L.M., Gundra, U.M., Cho, I., Bonneau, R., Blaser, M.J., Chua, K.H., Loke, P., 2014. Helminth colonization is associated with increased diversity of the gut microbiota. PLoS Negl. Trop. Dis. 8, 1–15. Legendre, P., Anderson, M.J., 1999. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24. Legendre, P., Gallagher, E.D., 2001. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280. Lević, J., Petrović, T., Stanković, S., Ivanović, D., 2013. The incidence of Pyrenochaeta terrestris in root of different plant species in Serbia. Zb. Matice Srp. za Prir. Nauk. / Matica Srp. J. Nat. Sci. 51, 21–30. Li, F., Chen, L., Redmile-Gordon, M., Zhang, J., Zhang, C., Ning, Q., Li, W., 2018. Mortierella elongata’s roles in organic agriculture and crop growth promotion in a mineral soil. Land Degrad. Dev. 1642–1651. Liu, X., Herbert, S.J., 2002. Fifteen years of research examining cultivation of continuous soybean in northeast China: a review. Field Crop. Res. 79, 1–7. Liu, S., Su, H., Su, X., Zhang, F., Liao, G., Yang, X., 2014. Arthrobotrys xiangyunensis, a novel nematode-trapping taxon from a hot-spring in Yunnan Province, China. Phytotaxa 174, 89–96. Liu, Y., Shi, G., Mao, L., Cheng, G., Jiang, S., Ma, X., An, L., Du, G., Collins Johnson, N., Feng, H., 2012. Direct and indirect influences of 8 yr of nitrogen and phosphorus fertilization on Glomeromycota in an alpine meadow ecosystem. New Phytol. 194, 523–535. Malapi-Wight, M., Salgado-Salazar, C., Demers, J., Veltri, D., Crouch, J.A., 2015. Draft genome sequence of Dactylonectria macrodidyma, a plant-pathogenic fungus in the Nectriaceae. Genome Announc. 3, 1–2. Marin-Felix, Y., Groenewald, J.Z., Cai, L., Chen, Q., Marincowitz, S., Barnes, I., Bensch, K., Braun, U., Camporesi, E., Damm, U., de Beer, Z.W., Dissanayake, A., Edwards, J., Giraldo, A., Hernández-Restrepo, M., Hyde, K.D., Jayawardena, R.S., Lombard, L., Luangsa-ard, J., McTaggart, A.R., Rossman, A.Y., Sandoval-Denis, M., Shen, M., Shivas, R.G., Tan, Y.P., van der Linde, E.J., Wingfield, M.J., Wood, A.R., Zhang, J.Q.,

13

Applied Soil Ecology xxx (xxxx) xxxx

N. Strom, et al.

Rosseel, Y., 2014. Lavaan: an R package for structural equation modeling. Present. London Sch. Econ. 48, 1–21. Rousseau, G., Rioux, S., Dostaler, D., 2007. Effect of crop rotation and soil amendments on Sclerotinia stem rot on soybean in two soils. Can. J. Plant Sci. 87, 605–614. RStudio Team, 2016. RStudio: Integrated Development Environment for R. Rupe, J.C., Robbins, R.T., Gbur, E.E., 1997. Effect of crop rotation on soil population densities of Fusarium solani and Heterodera glycines and on the development of sudden death syndrome of soybean. Crop Prot. 16, 575–580. Salvagiotti, F., Cassman, K.G., Specht, J.E., Walters, D.T., Weiss, A., Dobermann, A., 2008. Nitrogen uptake, fixation and response to fertilizer N in soybeans: a review. Field Crop. Res. 108, 1–13. Shipton, P.J., 1973. Occurrence and transfer of a biological factor in soil that suppresses take-all of wheat in Eastern Washington. Phytopathology 63, 511. Siddiqui, Z.A., Pichtel, J., 2008. Mycorrhizae: an overview. In: Siddiqui, Z.A., Akhtar, M.S., Futai, K. (Eds.), Mycorrhizae: Sustainable Agriculture and Forestry. Springer, Dordrecht, pp. 1–35. Singh, H.P., Batish, D.R., Kohli, R.K., 1999. Autotoxicity: concept, organisms, and ecological significance. Crit. Rev. Plant Sci. 18, 757–772. Soil Survey Staff, 2014. Keys to Soil Taxonomy, 12th ed. USDA-Natural Resources Conservation Service, Washington, DC. Song, Z., Shen, L., Zhong, Q., Yin, Y., Wang, Z., 2016. Liquid culture production of microsclerotia of Purpureocillium lilacinum for use as bionematicide. Nematology 18, 719–726. Southern Research and Outreach Center, 2015. Weather summary. Waseca, MN, USA. Southern Research and Outreach Center, 2016. Weather summary. Waseca, MN, USA. Spatafora, J.W., Chang, Y., Benny, G.L., Lazarus, K., Smith, M.E., Berbee, M.L., Bonito, G., Corradi, N., Grigoriev, I., Gryganskyi, A., James, T.Y., O’Donnell, K., Roberson, R.W., Taylor, T.N., Uehling, J., Vilgalys, R., White, M.M., Stajich, J.E., 2016. A phylumlevel phylogenetic classification of zygomycete fungi based on genome-scale data. Mycologia 108, 1028–1046. Standen, J.H., 1945. Variability and pathogenicity of Nigrospora oryzae (B. and Br.) Petch in maize. Phytopathology 35, 552–564. Stiles, C., Glawe, D., 1989. Colonization of soybean roots by fungi isolated from cysts of Heterodera glycines. Mycologia 81, 797–799. Sugawara, F., Strobel, G., Strange, R.N., Siedow, J.N., Van Duyne, G.D., Clardy, J., 1987. Phytotoxins from the pathogenic fungi Drechslera maydis and Drechslera sorghicola. Proc. Natl. Acad. Sci. U. S. A. 84, 3081–3085. Tukey, J.W., 1949. Comparing individual means in the analysis of variance. Biometrics 5, 99–114. Turco, R.F., Bischoff, M., Breakwell, D.P., Griffith, D.R., 1990. Contribution of soil-borne bacteria to the rotation effect in corn. Plant Soil 122, 115–120. U.S. Department of Agriculture National Agricultural Statistics Service, 2017. Acreage [WWW Document]. URL. https://www.usda.gov/nass/PUBS/TODAYRPT/ acrg0615.pdf. UNITE Community, 2017. Full UNITE+INSD dataset. Version 01.12.2017 [WWW Document]. UNITE Community. URL https://doi.org/10.15156/BIO/587474 (Accessed 2 March 2018). Wang, Jinli, Li, X., Zhang, J., Yao, T., Wei, D., Wang, Y., Wang, Jingguo, 2012. Effect of root exudates on beneficial microorganisms — evidence from a continuous soybean monoculture. Plant Ecol. 213, 1883–1892. Weiss, S., Xu, Z.Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., Lozupone, C., Zaneveld, J.R., Vázquez-Baeza, Y., Birmingham, A., Hyde, E.R., Knight, R., 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27. Westphal, A., Becker, J.O., 1999. Biological suppression and natural population decline of Heterodera schachtii in a California field. Phytopathology 89, 434–440. Whiting, K.R., Crookston, R.K., 1993. Host-specific pathogens do not account for the cornsoybean rotation effect. Crop Sci. 33, 539–543. Wu, L., Chen, J., Wu, H., Wang, J., Wu, Y., Lin, S., Khan, M.U., Zhang, Z., Lin, W., 2016. Effects of consecutive monoculture of Pseudostellaria heterophylla on soil fungal community as determined by pyrosequencing. Sci. Rep. 6, 1–10. Xin, X., Qin, S., Zhang, J., Zhu, A., Yang, W., Zhang, X., 2017. Yield, phosphorus use efficiency and balance response to substituting long-term chemical fertilizer use with organic manure in a wheat-maize system. Field Crop. Res. 208, 27–33. Xiong, W., Zhao, Q., Zhao, J., Xun, W., Li, R., Zhang, R., Wu, H., Shen, Q., 2014. Different continuous cropping spans significantly affect microbial community membership and structure in a vanilla-grown soil as revealed by deep pyrosequencing. Microb. Ecol. 70, 209–218. Yadav, V., Kumar, M., Deep, A.K., Kumar, H., Sharma, R., Tripathi, T., Tuteja, N., Saxena, A.K., Johri, A.K., 2010. A phosphate transporter from the root endophytic fungus Piriformospora indica plays a role in phosphate transport to the host plant. J. Biol. Chem. 285, 26532–26544. Yu, Z., Mo, M., Zhang, Y., Zhang, K., 2014. Taxonomy of nematode-trapping fungi from Orbiliaceae, Ascomycota. In: Zhang, K.-Q., Hyde, K.D. (Eds.), Nematode-Trapping Fungi. Springer, Dordrecht Heidelberg New York London, pp. 41–210. Zhang, F., Shen, J., Zhang, J., Zuo, Y., Li, L., Chen, X., 2010. Rhizosphere processes and management for improving nutrient use efficiency and crop productivity: implications for China. In: Sparks, D.L. (Ed.), Advances in Agronomy. Elsevier Inc, San Diego, CA, pp. 1–32. Zhang, L., Yang, J., Niu, Q., Zhao, X., Ye, F., Liang, L., Zhang, K.Q., 2008. Investigation on the infection mechanism of the fungus Clonostachys rosea against nematodes using the green fluorescent protein. Appl. Microbiol. Biotechnol. 78, 983–990. Zhao, Q., Xiong, W., Xing, Y., Sun, Y., Lin, X., Dong, Y., 2018. Long-term coffee monoculture alters soil chemical properties and microbial communities. Sci. Rep. 8, 1–11.

Zhang, Y., Crous, P.W., 2017. Genera of phytopathogenic fungi: GOPHY 1. Stud. Mycol. 86, 99–216. Martín-Fernández, J.A., Barceló-Vidal, C., Pawlowsky-Glahn, V., 2003. Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Math. Geol. 35, 253–278. Mcdonald, D., Hyde, E., Debelius, J.W., Morton, J.T., Gonzalez, A., Ackermann, G., Aksenov, A.A., Behsaz, B., Brennan, C., Chen, Y., Goldasich, D., Dorrestein, P.C., Dunn, R.R., Fahimipour, A.K., Gaffney, J., Gilbert, J.A., Gogul, G., Green, J.L., Hugenholtz, P., Humphrey, G., Huttenhower, C., Jackson, M.A., Janssen, S., Jeste, D.V., Jiang, L., Kelley, S.T., Knights, D., Kosciolek, T., Ladau, J., Leach, J., Marotz, C., Meleshko, D., Melnik, A.V., Metcalf, J.L., Mohimani, H., Montassier, E., Rahnavard, G., Robbins-pianka, A., Sangwan, N., Shorenstein, J., Smarr, L., Vázquez-baeza, Y., Vrbanac, A., Wischmeyer, P., Wolfe, E., Zhu, Q., Gut, A., Jt, M., Gonzalez, A., Ackermann, G., Behsaz, B., Brennan, C., Chen, Y., Goldasich, L., Pc, D., Rr, D., Jl, G., Hugenholtz, P., Humphrey, G., Huttenhower, C., Ma, J., Janssen, S., Jiang, L., St, K., Knights, D., Kosciolek, T., 2018. American gut: an open platform for citizen science. mSystems 3, 1–28. McMurdie, P.J., Holmes, S., 2013. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, 1–11. Meade, B., Puricelli, E., Mcbride, W., Valdes, C., Hoffman, L., Foreman, L., Dohlman, E., 2016. Corn and soybean production costs and export competitiveness in Argentina, Brazil, and the United States. Econ. Res. Serv. EIB-154 1–52. Mendiburu, Fde, 2017. Agricolae: Statistical Procedures for Agricultural Research. R Package Version 1.2-8 [WWW Document]. URL. https://cran.r-project.org/ package=agricolae. Miller, M.H., 2000. Arbuscular mycorrhizae and the phosphorus nutrition of maize: a review of Guelph studies. Can. J. Plant Sci. 80, 47–52. Mills, K.E., Bever, J.D., 1998. Maintenance of diversity within plant communities: soil pathogens as agents of negative feedback. Ecology 79, 1595–1601. Minnesota Department of Natural Resources, 2019. Climate’s Impact on Water Availability [WWW Document]. Climate. URL https://www.dnr.state.mn.us/climate/water_availability.html (Accessed 20 August 2018). . Minnesota Department of Natural Resources, 2017. Climate [WWW Document]. Minnesota Facts Fig. URL https://www.dnr.state.mn.us/faq/mnfacts/climate.html (Accessed 20 August 2018). . Nehl, D.B., Allen, S.J., Brown, J.F., 1996. Deleterious rhizosphere bacteria: an integrating perspective. Appl. Soil Ecol. 5, 1–20. Nickel, S.E., Crookston, R.K., Russelle, M.P., 1995. Root growth and distribution are affected by corn-soybean cropping sequence. Agron. J. 87, 895–902. O’Donnell, K., Sutton, D.A., Fothergill, A., McCarthy, D., Rinaldi, M.G., Brandt, M.E., Zhang, N., Geiser, D.M., 2008. Molecular phylogenetic diversity, multilocus haplotype nomenclature, and in vitro antifungal resistance within the Fusarium solani species complex. J. Clin. Microbiol. 46, 2477–2490. Oksanen, J., 2015. Multivariate Analysis of Ecological Communities in R: Vegan Tutorial. R Doc. 43. Oksanen, J., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O’Hara, R., Simpson, G., Solymos, P., Stevens, M., Wagner, H., 2016. Vegan: Community Ecology Package. R Package Version 2.3-4. [WWW Document]. URL. https://cran.r-project.org/ package=vegan. Olsson, S., Alström, S., 1996. Plant-affecting streptomycin-sensitive micro-organisms in barley monoculture soils. New Phytol. 133, 245–252. Osorio, N.W., Habte, M., 2014. Soil phosphate desorption induced by a phosphate-solubilizing fungus. Commun. Soil Sci. Plant Anal. 45, 451–460. Palmer, J.M., Jusino, M.A., Banik, M.T., Lindner, D.L., 2018. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, 1–27. Pankhurst, C.E., Magarey, R.C., Stirling, G.R., Blair, B.L., Bell, M.J., Garside, A.L., 2003. Management practices to improve soil health and reduce the effects of detrimental soil biota associated with yield decline of sugarcane in Queensland, Australia. Soil Tillage Res. 72, 125–137. Pedersen, P., Grau, C.R., 2010. Effect of agronomic practices and soybean growth stage on the colonization of basal stems and taproots by Diaporthe phaseolorum var. sojae. Crop Sci. 50, 718–722. Pedersen, P., Tylka, G.L., Mallarino, A., Macguidwin, A.E., Koval, N.C., Grau, C.R., 2010. Correlation between soil pH, Heterodera glycines population densities, and soybean yield. Crop Sci. 50, 1458–1464. Persmark, L., 1996. Population dynamics of nematophagous fungi and nematodes in an arable soil: vertical and seasonal fluctuations. Soil Biol. Biochem. 28, 1005–1014. Philippot, L., Raaijmakers, J.M., Lemanceau, P., van der Putten, W.H., 2013. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799. Plaza, D.F., Schmieder, S.S., Lipzen, A., Lindquist, E., Künzler, M., 2016. Identification of a novel nematotoxic protein by challenging the model mushroom Coprinopsis cinerea with a fungivorous nematode. Genes Genomes Genetics 6, 87–98. Quaedvlieg, W., Verkley, G.J.M., Shin, H.D., Barreto, R.W., Alfenas, A.C., Swart, W.J., Groenewald, J.Z., Crous, P.W., 2013. Sizing up Septoria. Stud. Mycol. 75, 307–390. R Core Team, 2018. R: A Language and Environment for Statistical Computing. [WWW Document]. URL. https://www.r-project.org/. Riedell, W.E., Schumacher, T.E., Clay, S.A., Ellsbury, M.M., Pravecek, M., Evenson, P.D., 1998. Corn and soil fertility responses to crop rotation with low, medium, or high inputs. Crop Sci. 38, 427–433. Robertson, A.E., Munkvold, G.P., Hurburgh, C.R., Ensley, S., 2011. Effects of natural hail damage on ear rots, mycotoxins, and grain quality characteristics of corn. Agron. J. 103, 193–199.

14