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Atmospheric Environment

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Dust-associated microbiomes from dryland wheat fields differ with tillagepractice and biosolids application

Daniel C. Schlattera, William F. Schillingerb, Andy I. Baryc, Brenton Sharrattd,Timothy C. Paulitza,∗

aUSDA-ARS, Wheat Health, Genetics and Quality Research Unit, Pullman, WA 99164, USAbDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USAcWashington State University, Puyallup Research and Extension Center, Puyallup, WA 98371, USAdUSDA-ARS, Northwest Sustainable Agroecosystems Research Unit, Pullman, WA 99164, USA

A R T I C L E I N F O

Keywords:BiosolidsDustFungiBacteriaMicrobiomeWheatUndercutter conservation tillageDisk conventional tillage

A B S T R A C T

Wind erosion is a significant threat to the productivity and sustainability of agricultural soils. In the drylandwinter wheat (Triticum aestivum L.)-fallow region of Inland Pacific Northwest of the USA (PNW), farmers in-creasingly use conservation tillage practices to control wind erosion. In addition, some farmers in this dry regionapply municipal biosolids to soils as fertilizer and a source of stable organic matter. The impacts of soil man-agement practices on emissions of dust microbiota to the atmosphere are understudied. We used high-throughput DNA sequencing to examine the impacts of conservation tillage and biosolids amendments on thetransport of dust-associated fungal and bacterial communities during simulated high-wind events over two yearsat Lind, WA. The fungal and bacterial communities contained in windblown dust differed significantly withtillage (conservation vs. conventional) and fertilizer (synthetic vs. biosolids) treatments. However, the richnessand diversity of fungal and bacterial communities of dust did not vary significantly with tillage or fertilizertreatments. Taxa enriched in dust from fields under conservation tillage represented many plant-associated taxathat likely grow on residue left on the soil surface, whereas taxa that were more abundant with conventionaltillage were those that likely grow on buried plant residue. Dust from biosolids-amended fields harbored greaterabundances of taxa that likely feed on introduced carbon. Most human-associated taxa that may pose a healthrisk were not present in dust after biosolids amendment, although members of Clostridiaceae were enriched withthis treatment. Results show that tillage and fertilizer management practices impact the composition ofbioaerosols emitted during high-wind events and have potential implications for plant and human health.

1. Introduction

Wind erosion from agricultural land is a major threat to the soilresource and hampers our ability to sustainably feed an escalatinghuman population (Pimentel, 2003). Wind erosion occurs primarily inarid and semi-arid ecosystems with an estimated 363 teragrams (Tg,1× 109 kg) of soil being eroded from anthropogenic or agriculturallandscapes per year (Ginoux et al., 2012). Airborne sediment, especiallythe finest fraction (< 10 microns), can be transported long distances inthe upper atmosphere (Acosta-Martínez et al., 2015; Favet et al., 2013;Prospero et al., 2014). Wind erosion and blowing dust emissions com-monly occur in the dryland PNW wheat-fallow area of the ColumbiaPlateau east of the Cascade Mountains due to drought, low productionof crop residue, excessive tillage, and poorly-aggregated soils with loworganic matter. This 1.56 million-hectare cropland region receives<

300mm average annual precipitation and was a native sagebrush-grassland ecosystem before being converted to wheat farming 135 yearsago. Today, the PNW is the major soft-white wheat producing region inthe USA. Winter wheat-summer fallow is the principal cropping systemwhere only one wheat crop is produced on a given parcel of land everytwo years (Schillinger and Papendick, 2008). During the fallow year,primary tillage is conducted in April or May to sever capillary pores andchannels to limit loss of soil water accumulated in the winter during thehot, dry summer months (Wuest and Schillinger, 2011). Many farmerspractice conservation tillage as conventional tillage generally buriesmost surface residues, can pulverize soil clods, and reduce surfaceroughness; all of which leaves soil vulnerable to wind erosion (Sharrattand Schillinger, 2016; Singh et al., 2012). Especially problematic in thePNW is emission of small dust particles ≤10 μm in diameter, known asPM10. Emission of PM10 during wind storms results in frequent

https://doi.org/10.1016/j.atmosenv.2018.04.030Received 16 October 2017; Received in revised form 17 April 2018; Accepted 19 April 2018

∗ Corresponding author.E-mail address: timothy.paulitz@ars.usda.gov (T.C. Paulitz).

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exceedances of the PM10 Air Quality Standard established by the USEnvironmental Protection Agency (EPA) (Sharratt and Lauer, 2006).

No-till and conservation-till practices have been advocated for thePNW drylands. No-till fallow, where herbicides are used in lieu of til-lage to control weeds, is ideal for erosion control and is used by anincreasing number of dryland farmers (Schillinger and Young, 2014)despite being generally less efficient than tillage-based fallow for re-tention of stored seed-zone soil water (Papendick, 2004). One methodfor conservation tillage in this region of the USA is the use of an un-dercutter implement as an alternative to the conventional disk or fieldcultivator implements (Young and Schillinger, 2012). The undercutterhas wide, narrow-pitch sweep blades that slice beneath the soil at adesired depth to create a tillage mulch and retain significantly moresurface residue and soil clods compared to conventional-tillage methods(Papendick, 2004; Sharratt et al., 2012; Sharratt and Feng, 2009).

In addition to conservation tillage, organic amendments to the soilhave been used worldwide to fertilize soils, reduce erosion, and im-prove soil heath (Diacono and Montemurro, 2010; Medina et al., 2015;Reardon and Wuest, 2016). Biosolids are produced from sewage was-tewater where the solid fraction is separated, digested to stabilize or-ganic matter and reduce pathogen loads, and concentrated prior to fieldapplication (Lu et al., 2012). Biosolids have high concentration of or-ganic matter, N, P, and several plant micronutrients (Lu et al., 2012;Rigby et al., 2016; Stehouwer et al., 2000). Biosolids are increasinglyapplied to agricultural fields as an alternative to synthetic fertilizersand to enhance soil aggregate size and stability (Brown et al., 2011;Cogger et al., 2013; Powlson et al., 2012). Despite the approval of theEPA and state environmental agencies, concerns exist regarding thepotential survival and transmission of pathogenic microbes derivedfrom biosolids after they are applied to agricultural land (Brooks et al.,2004, 2007; Dowd et al., 1997; Dowd, 2003).

Dust emitted from agricultural soils harbors diverse microbialcommunities of fungi, bacteria, and viruses that are aerosolized andtransported as spores, cell aggregates, or with dust particles (Despréset al., 2012; Fröhlich-Nowoisky et al., 2016; Gandolfi et al., 2013). Dustparticles can provide microhabitats that protect microbial propagulesfrom inhospitable conditions, such as damaging ultraviolet radiation ordesiccation, and provide organic substrates that allow microbes tosurvive long-distance transport (Acosta-Martínez et al., 2015; Fröhlich-Nowoisky et al., 2016). Moreover, many airborne bacteria and fungi arewell adapted to survive atmospheric transport by producing re-calcitrant, desiccation-resistant spores or ice-nucleation surface pro-teins. Because many of these microbial taxa play critical roles as plantand animal pathogens and symbionts, allergens, and carbon and nu-trient cyclers, the microbial consortia dispersed with windblown dustare hypothesized to have significant consequences for plant, human,and ecosystem health (Acosta-Martínez et al., 2015; Déportes et al.,1995; Mazar et al., 2016). For example, high atmospheric PM10 con-centrations have been linked to increases in the incidence of fungal andbacterial infections (Abdel Hameed and Khodr, 2001; Cummings et al.,2010; Garcia-Chevesich et al., 2014; Goudie, 2014; Jusot et al., 2017;Sprigg et al., 2014; Weir-Brush et al., 2004).

Agricultural management practices such as tillage, organic amend-ments, and fertilizers, can have significant impacts on the diversity andcomposition of soil microbial communities (Sharma-Poudyal et al.,2017; Yin et al., 2017). As a result, microbial communities inhabitingwindblown sediment will reflect the impacts of management practiceson soil communities (Bowers et al., 2011). Although it is recognizedthat assemblages transported with windblown sediment are highlydynamic and related to broad land-use and climatic variables (Barberánet al., 2015; Bowers et al., 2011; Fröhlich-Nowoisky et al., 2016), wehave little understanding of how specific methods of tillage or fertili-zation impact the aerial dispersal of microbial communities duringwind erosion events.

In this work, we examine how tillage method (conventional vs.conservation) and fertilization method (synthetic vs. biosolids) impact

the microbial communities of windblown sediment in the drylandwheat-fallow cropping region of the PNW. Specifically, we hypothesizethat different tillage and fertilization methods will modify the compo-sition of bacterial and fungal assemblages in windblown dust, reflectingshifts in microbial communities in soil resulting from these practices.We also hypothesize that dust will reflect both communities originatingfrom the soil and from the fresh biosolids, as well as those microbesfrom the soil that colonize the applied biosolids.

2. Methodology

2.1. Experimental design

Field plots were sampled at the Washington State UniversityDryland Research Station near Lind, Washington (47°00′N, 118 °34′W)in 2015 and 2016. This site receives 242mm average annual pre-cipitation. The soil is a Shano silt loam (coarse-silty, mixed, superactive,mesic, Xeric Haplocambids) composed of 10% clay, 51% silt, and 39%fine sand. Organic matter content in the surface 15 cm is 0.7%. The soilpH is 6.7. Experimental treatments were established in a split-blockdesign with tillage (conventional vs. conservation) as the main-plotfactor and fertilizer (synthetic vs. biosolids) as the subplot factor. Twosets of plots were established to allow for data collection every year inthe 2-year winter wheat-fallow rotation. Size of individual main plotswas 76×8m and subplots were 38× 8m. Each treatment combina-tion was replicated four times in a randomized complete block design.Glyphosate [N-(phosphonomethyl) glycine] herbicide was applied inmid-March at a rate of 0.43 kg ae/ha to control weeds. Biosolids ma-terial (Class B) was obtained from the King County WastewaterTreatment Division, Seattle, WA, and applied to fallow fields with amanure spreader on 4 May 2015 and on 19 April 2016 (to a differentfield) at a rate of 6 508 kg/ha (dry weight), so that both phases wererepresented in each year. Each plot had a history of biosolids use: 2015plots first received biosolids in 2011 and 2016 plots first receivedbiosolids applications in 2012. Synthetic fertilizers were applied everyfallow year to meet the nutrient requirements of each subsequent wheatcrop. Biosolids were applied at the above-mentioned rate every fouryears to meet the nutrient requirements for two wheat crops.Conventional tillage was performed with a tandem disk implement andconservation tillage with an undercutter implement. Tillage was con-ducted immediately after biosolids application during both years.Liquid synthetic fertilizer at a rate of 56 kg N plus 11 kg S/ha was ap-plied to the soil for every wheat crop as liquid aqua NH3-N plus thiosolS in both the disk and undercutter treatments. The fertilizer was in-jected into the soil at a depth of 13 cm during primary spring tillagewith the undercutter implement. In the disk treatment, fertilizer wasapplied to the soil surface then incorporated into the soil. The plotswere tilled to a depth of 13 cm immediately (within 2 h) after surface-applying biosolids or synthetic fertilizer (disk treatment only, syntheticfertilizer was injected in the undercutter treatment) on 4 May 2015 and19 April 2016. Plots were subsequently rodweeded at a depth of 10 cmon 15 June 2015 and on 3 June and 10 July 2016.

2.2. Windblown sediment collection

A portable wind tunnel was used to simulate wind erosion processesthat naturally occur in the field. Windblown sediment was collectedfrom tillage and fertilizer treatments using the wind tunnel on 18 June2015 and on 6 June 2016. These dates were selected based on the dateof application and soil being most susceptible to wind erosion aftertillage in spring (Papendick, 2004). The portable wind tunnel has aworking section 7.3m long, 1.2m high and 1.0m wide. A description ofthe details about the wind tunnel design can be found in Pietersma et al.(1996). Airborne sediment was collected and wind speed was measuredat a distance of 5.4 m downwind of the leading edge of the workingsection; this distance ensured collection of airborne sediment within a

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fully developed boundary layer. Sediment was collected using a non-commercial isokinetic slot sampler (Stetler et al., 1997). The samplertraps sediment in saltation and suspension to a height of 0.75m abovethe soil surface and separates the sediment from the air stream using acyclone with a>88% efficiency (Horning et al., 1998). The slot sam-pler was calibrated in the wind tunnel prior to the experiment to ensureisokinetic conditions at a free stream velocity of 16m s−1 inside thetunnel. Wind speed was measured using pitot tubes and the wind tunnelwas operated at a free-stream wind speed of 16m s−1 as this speedoccurs every two years in the region (Wantz and Sinclair, 1981).Windblown sediment was collected over a 10-minute period re-presentative of active saltation conditions that normally occur in thefield during high wind events. To achieve saltation activity character-istic of field conditions inside the wind tunnel, an abrader (sand par-ticles 250–500 μm in diameter) was introduced into the airstream usinga metered feeding system. Abrader was introduced into the airstream ata constant rate of 0.5 gm−1 s−1 as this rate typifies the flux of soilduring high winds across the Columbia Plateau (Sharratt et al., 2007).Sediment (hereafter referred to as dust) was placed in a plastic bag andair dried at 30 °C prior to sealing the bag.

2.3. DNA extraction and Illumina sequencing

DNA was extracted from 0.1 g of dust samples using the MoBioPowerSoil kit (MoBio/Qiagen, Carlsbad, CA) according to the manu-facturer's instruction manual. Homogenization of samples was per-formed on a FastPrep bead beater (MP Biomedical, Santa Ana, CA)using the ‘soil’ program. DNA was quantified on a nanodrop spectro-photometer and submitted for amplification of the V1-V3 region of the16S rRNA gene and paired-end (2×300 bp) sequencing on theIllumina MiSeq platform using v3 chemistry at the University ofMinnesota Genomics Center (UMGC). PCR amplification at the UMGCused a dual-indexing approach as described in Gohl et al. (2016).Briefly, this consisted of a first round of PCR using template-specificprimers MN_27F (5′-AGAGTTTGATCMTGGCTCAG-3′) and MN_534R (5′-ATTACCGCGGCTGCTGG-3′) with KAPA HIFI Hot Start polymerase.Total PCR reaction volume was 6 μl. PCR conditions consisted of aninitial denaturing at 95 °C for 5min, followed by 25 cycles of 98 °C for20 s, 55 °C for 15 s, and 72 °C for 1min, with a final extension at 72 °Cfor 1min. Products from the first PCR were diluted 1:100 and 5 μl wasincluded in second PCR using indexing primers. The second PCR con-sisted of an initial denaturation at 95 °C for 5min, 10 cycles of 98 °C for20 s, 55 °C for 15 s, and 72 °C for 1min, with a final extension at 72 °Cfor 5min. Products were pooled, size selected, and spiked with 20%PhiX prior to sequencing.

The fungal ITS1 region was amplified with ITS1-F and ITS2 primersadapted to include linkers, barcoding tags, and additional bases to in-troduce a phase offset for Illumina sequencing (Supplemental Table 1).Primers were combined in equimolar ratio prior to PCR amplification.PCR reactions (20 μl total volume) consisted of 10 μl FastStart MasterMix (Roche), 0.7 μl of primers (10 μM), 8.3 μl PCR-grade H2O, and 1 μlof template DNA (1/10 diluted). Reactions were performed in triplicatewith three separate annealing temperatures (50, 53, or 55 °C). Ther-mocycling conditions consisted of an initial denaturation at 95 °C for4min, 30 cycles of 95C for 30 s, annealing at 50, 53, or 55 °C for 30 s,and extension at 72 °C for 45 s, with a final extension at 72 °C for 7min.PCR products were pooled and checked for amplification on a 1.5% gel.Illumina adapters and barcodes (provided by the University of Idaho)were added in an additional PCR step consisting of 10 μl 2x FastStartMaster Mix, 0.75 μl barcoding primers, 8.25 μl H2O, and 1 μl productfrom initial PCRs. Thermocycling for the barcoding PCR consisted of aninitial denaturation at 95 °C for 4min, 10 cycles of 95 °C for 30 s, 60 °Cfor 30 s, 72 °C for 1min, and a final extension at 72 °C for 7min. Suc-cessful addition of barcodes was confirmed by visualization of an ex-pected size shift on a 1.5% gel. Barcoded amplicons were quantifiedwith a PicoGreen fluorometry kit, pooled, and sequenced at the

University of Idaho Core Genomics Center (2×300 MiSeq, v3 chem-istry). Sample-free extraction controls were subjected to PCR for bothfungi and bacteria and sequenced with each run.

2.4. Sequence processing

Forward and reverse reads were paired using PEAR (v0.9.6) (Zhanget al., 2014). Barcodes and primer sequences were removed with cu-tadapt (v1.91) (Martin, 2011), and sequences with ambiguous bases orshorter than 350 bp (bacteria) or 200 bp (fungi) were removed. Pro-cessed sequences were clustered following the UPARSE pipeline (Edgar,2013) using vsearch (Rognes et al., 2016) for all steps with the ex-ception of operational taxonomic unit (OTU) clustering which usedusearch (v8.1) (Edgar, 2013). Briefly, reads were quality filtered using amaximum expected error rate of 1, dereplicated, and singletons re-moved prior to OTU clustering at 97% similarity threshold using thecluster_otus command. Processed reads were then mapped to OTUclusters to generate an OTU abundance table. For bacteria, taxonomywas assigned using OTU representative sequences (centroids) for bac-teria with the RDP Naïve Baysian Classifier (Wang et al., 2007) usingthe greengenes 13_8 reference database (DeSantis et al., 2006) and an80% confidence threshold. Bacterial OTUs were filtered to remove anynon-bacterial OTUs or those classified as mitochondria or chloroplastsusing QIIME scripts (v1.9.1) (Caporaso et al., 2010). For fungi, tax-onomy was assigned BLAST + to the UNITE database (31.1.2016general release) and any OTU with an e-value of> 10e-40, matchlength (subject/query) of< 75%, and a percent identity of< 80% to aUNITE representative was discarded. OTUs with a total sequence countof< 5 were removed to reduce poor quality OTUs and the OTU tablewas rarefied to 20,000 and 17,000 sequences/sample for bacteria andfungi, respectively, prior to analysis. Unrarefied OTU tables were re-tained for differential abundance analysis with DESeq2 (v1.12.4) (Loveet al., 2014). Material-free extraction controls indicated minimal cross-contamination of bacterial and fungal sequences in OTU tables prior torarefaction.

2.5. Data analysis

Non-metric multidimensional scaling was performed on Bray-Curtisdistances constructed from OTU tables to visualize relationships amongsamples using the metaMDS function in the vegan package in R(Oksanen et al., 2016). PERMANOVAs were used to test for differencesin community structure between years, tillage treatments, and fertilizerregimes using the adonis function. Chemical characteristics of dustsamples, as described in Pi et al. (2018a), were evaluated for their re-lation to community differentiation using the ‘envfit’ function of vegan.Briefly, data were scaled to have a mean of 0 and a standard deviationof 1 and significant vectors (1 000 permutations) were fitted to NMDSaxes. Richness, Shannon diversity (H′), and the inverse Simpson's indexof diversity (1/D) of bacterial and fungal communities were calculatedwith vegan and compared among years, tillage treatments, and ferti-lizer treatments using ANOVA and Tukey's Honest Significance post-hoctest using the agricolae R package (de Mendiburu, 2016). Abundantgenera (> 0.05% total sequence abundance) were tested for differentialabundance between tillage and fertilizer treatments (model:∼-Year + Tillage x Fertilizer) using ANOVA on Log10 (1 + x) trans-formed sequence counts with a Benjamini-Hochberg correction for falsediscovery rate (FDR) (Benjamini and Hochberg, 1995). DESeq2 wasused to identify taxa (OTUs) that differed between tillage and fertilizertreatments. Briefly, unrarefied OTU tables were filtered to remove lowabundance taxa (< 10 total counts) and those found in fewer than 3samples. Tillage and fertilizer factors were combined and OTUs thatdiffered significantly between tillage and fertilizer treatments wereevaluated using the model (∼Year + Factor) with a Wald test tocontrast OTUs among treatments. Fungal OTUs with a base (normal-ized) mean of> 50 and a log2-fold difference between treatments

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of> 2 were considered to be differentially abundant. For bacteria,OTUs with a base mean of> 20 and a log2-fold difference of> 1.5were considered to be differentially abundant.

3. Results

We obtained 2,444,425 fungal ITS1 sequences representing 1 204OTUs with 17,118 to 242,272 (mean of 56,939 ± 39,290) sequencesper sample prior to rarefaction. Fungal communities in dust weredominated by Ascomycota (84.7 ± 6.4%), followed by Basidiomycota(7.9 ± 4.5%), unidentified phyla (3.9 ± 5.5%), and Zygomycota(2.7 ± 3.0%). For bacteria, we obtained 4,477,439 sequences be-longing to 7813 OTUs at 97% similarity with 21,691 to 85,124 se-quences per sample (mean of 45,227 ± 12,841) sequences prior torarefaction. Bacteria in dust samples represented diverse phyla, pri-marily belonging to Actinobacteria (36.6 ± 18.2%), Proteobacteria(23.1 ± 8.2%), Bacteroidetes (8.6 ± 5.3%), Chloroflexi(7.4 ± 5.4%), Firmicutes (6.3 ± 12.7%), Planctomycetes(5.1 ± 3.7%), Gemmatimonadetes (4.7 ± 3.8%), Acidobacteria(3.9 ± 2.4%), and Verrucomicrobia (1.9 ± 1.2%).

3.1. Microbial diversity of dust under different tillage and fertilization

After rarefaction, individual dust samples contained from 256 to432 fungal OTUs classified to 78–117 genera. The most abundantfungal OTUs were related to Fusarium (OTU1; 5.48 ± 3.51% of se-quences/sample), Ulocladium (OTU 2; 4.89 ± 2.73% of sequences),unidentified Ascomycota (OTU 9; 4.26 ± 5.55% of sequences), andChaetomiaceae (OTU 6; 4.09 ± 3.02% of sequences) species (Fig. 1).Samples contained from 1 172 to 2 832 bacterial OTUs classified into90–148 genera. The most abundant bacterial OTUs were related toModestobacter (OTU33; 0.018 ± 0.006% of sequences/sample),

Nocardiodiaceae (OTU40; 0.014 ± 0.004% of sequences), Kaistobacter(OTU6; 0.014 ± 0.004%), Amycolatopsis (OTU8; 0.01 ± 0.008%), andBradyrhizobium (OTU2; 0.009 ± 0.003%) (Fig. 2). The richness anddiversity of fungal and bacterial communities of dust did not vary sig-nificantly (ANOVA was p > 0.05 for all factors) with tillage or ferti-lizer treatments.

3.2. Structure of dust microbial communities vary with tillage and fertilizerapplications

The composition of microbial communities in windblown dustvaried among tillage and fertilizer treatments. Fungal communitiesclustered primarily by experiment year and tillage, though fertilizertreatment was also a significant factor (Fig. 3; Table 1). In contrast,variation in dust bacterial communities depended primarily on theexperiment year, though tillage and fertilizer treatments each had sig-nificant effects (Table 1, Figs. 3 and 4). Moreover, there was a sig-nificant interaction between year and fertilizer treatments, wherecommunities from the 2016 sampling, but not 2015, were clearly dif-ferentiated by synthetic or biosolids fertilizer method (Figs. 3 and 4,Table 1). Thus, both tillage and fertilization practices contribute sig-nificantly to the microbial composition of dust emitted from erodingagricultural soils, though the ways in which microbial communitiesrespond to these factors may also vary from year to year.

3.3. Impact of tillage on microbial communities of dust

Windblown dust had higher frequencies of Ascomycota in conven-tional versus conservation tillage treatments (87.2 ± 5.3 and82.0 ± 6.5% respectively; KW-test, p= 0.004), whereas dust fromconservation tillage plots had greater portions of unidentified phyla(6.2 ± 7.4 and 1.9 ± 1.0% for conservation and conventional tillage,

Fig. 1. Heatmap of the relative abundances (Log2 (1 + X)-transformed sequence counts) of the 20 most abundant fungal OTUs. Fert = fertilized with syntheticfertilizer. Bio = fertilized with biosolids.

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respectively; KW-test p < 0.0001). Tillage treatment had little impacton the relative abundance of bacterial phyla and only Nitrospiraetended to be relatively more abundant with conventional tillage (con-ventional: 0.11 ± 0.05%; conservation: 0.07 ± 0.05%; KW-testp= 0.03), though this phylum did not differ significantly after cor-rection for multiple comparisons (FDR p > 0.1). At the genus-level,taxa belonging to Chaetomidium, Chaetomium, Coprinellus, Fusarium,Macroventuria, Microdochium, Mortierella, Trichoderma, and Ulocladiumcomprised larger portions of dust fungi with conventional tillage,whereas those belonging to Cadophora, Elasticomyces, andPhaeococcomyces were more prevalent in dust with conservation tillage(Table 2). Considering bacterial genera, members of Nocardiodes,Streptomyces, and Kaistobacter were significantly more abundant in dustfrom conventional tillage. In contrast, other groups, such as Friedma-niella, Modestobacter, Hymenobacter, and Methylobacterium, were moreprevalent in dust from conservation tillage treatment (Table 3).

Fig. 2. Heatmap of the relative abundances (Log2 (1 + X)-transformed sequence counts) of the 20 most abundant bacterial OTUs. g = genus, f = family, ando = order. Fert = fertilized with synthetic fertilizer. Bio = fertilized with biosolids.

Fig. 3. Non-metric multidimensional scaling (NMDS) of fungal communities ofdust samples from different tillage and fertilization treatments. Arrows indicatefitted vectors significantly (p < 0.05) correlated with dust chemical char-acteristics. Fert= fertilized with synthetic fertilizer. Bio= fertilized with bio-solids.

Table 1PERMANOVA results and p-values of year, tillage, and fertilizer factors basedon 1 000 permutations.

Fungi Bacteria

Factor r2 p-val r2 p-val

Year (Y) 0.112 0.001 0.289 0.001Tillage (T) 0.144 0.001 0.068 0.011Fertilizer (F) 0.053 0.014 0.056 0.03Y x T 0.0299 0.188 0.027 0.242Y x F 0.038 0.072 0.05 0.031T x F 0.034 0.115 0.024 0.34Y x F x T 0.031 0.161 0.023 0.38

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3.4. Impact of fertilization method on microbial communities of dust

Microbial communities on dust emitted from plots fertilized withbiosolids had significantly greater portions of Zygomycota than syn-thetically-fertilized plots (4.12 ± 3.6% and 3.6 ± 1.2%, respectively;KW-test p= 0.0007). Similarly, few bacterial phyla were impacted

significantly by fertilizer treatment, where only the Firmicutes (bioso-lids: 7.1 ± 3.9%; synthetic: 1.8 ± 0.09%; KW-test p < 0.0001), andProteobacteria (biosolids: 24.7 ± 2.6%; synthetic: 22.2 ± 2.3%;p=0.02) differed in relative abundance. However, only Firmicutes,which was of greater abundance in dust from biosolids versus synthe-tically fertilized plots, was significant after correction for false dis-covery (FDR p=0.002). Among fungal genera impacted by fertilizertreatment, Fusarium, Mortierella, and Ulocladium were relatively moreabundant in windblown dust from plots fertilized with biosolids,whereas Cadophora and Chaetomidium were more common in those withsynthetic fertilizer (Table 2). In some cases, such as Chaetomidium,Cladophialophora, Coniochaeta, Fusarium, Microdochium, Mortierella, andTrichoderma, there were greater differences in abundances among til-lage treatments when synthetic fertilizer was used versus biosolids.Bacterial genera influenced by fertilization method included Janthino-bacterium, Mycobacterium, and Streptomyces, which comprised greaterportions of dust communities in plots fertilized with biosolids, andDA101 (phylum Verrucomicrobia), which was more dominant in dustfrom plots treated with synthetic fertilizer (Table 3).

Among fungal families commonly associated with the human intestinaltract (Saccharomycetaceae and Davidiellaceae), only Davidiellaceae was de-tected (0.018% vs 0.002% in dust emitted from biosolids versus syntheticallyfertilized plots, respectively), though differences between fertilizer treatmentswere not significant (data not shown). Similarly, among bacterial familiesthat commonly dominate human-gut communities (Bacteroidaceae,Clostridiaceae, Enterobacteriaceae, Eubacteriaceae, Peptococcaceae,Peptostreptococcacea, Rumiococcaceae, and Streptococcaceae), onlyClostridiaceae, Enterobacteriaceae, and Peptostreptococcaceae, were detectedin dust communities. However, members of these families were very rare(Clostridiaceae 0.005% vs 0.0005% of sequences; Enterobacteriaceae0.0005% vs 0.0009% of sequences; Peptostreptococcaceae<0.0001% vs 0%of sequences in dust from biosolids-treated versus synthetically fertilized plotson average) and only members of the family Clostridiaceae were significantlyenriched when biosolids were applied (ANOVA F=38.06, p < 0.0001).

Fig. 4. Non-metric multidimensional scaling (NMDS) bacterial communities ofdust samples from different tillage and fertilization treatments. Arrows indicatefitted vectors significantly (p < 0.05) correlated with dust chemical char-acteristics. Fert= fertilized with synthetic fertilizer. Bio= fertilized with bio-solids.

Table 2Mean percentage of sequences (± standard deviation) of abundant fungal genera in dust. ANOVA p-values marked with * are significant after an FDR correction(FDR p < 0.1).

Genus 2015 2016 ANOVA p-value

Conventional Conservation Conservation

Fert Bio Bio Fert Bio Fert Bio Tillage Fertilizer Tillage xFertilizer

Alternaria 5.43 ± 2.64 5.78 ± 1.24 8.55 ± 2.73 4.47 ± 0.77 2.39 ± 0.34 1.88 ± 1.72 2.11 ± 1.14 2.64 ± 0.76 0.3702 0.3189 0.9066Cadophora 0.69 ± 0.63 0.46 ± 0.24 1.68 ± 1.7 0.42 ± 0.11 0.26 ± 0.22 0.15 ± 0.08 1.08 ± 0.97 0.56 ± 0.45 0.0079 0.0626 0.3907Chaetomidium 0.96 ± 0.35 1.75 ± 0.31 0.25 ± 0.12 0.87 ± 0.39 1 ± 0.35 1.24 ± 1.27 0.47 ± 0.51 1.24 ± 1 0.0013* 0.0015* 0.0232Chaetomium 5.39 ± 2.21 3.72 ± 0.87 1.02 ± 1.04 1.24 ± 0.61 4.59 ± 0.51 2.25 ± 2 1.09 ± 0.83 1.66 ± 1.94 < 0.0001* 0.4188 0.0628Cistella 0.12 ± 0.12 0.02 ± 0.02 0.12 ± 0.16 0.68 ± 1.32 0.73 ± 0.95 2.65 ± 3.63 0.38 ± 0.21 1.31 ± 1.06 0.5089 0.6902 0.2323Cladophialophora 1.5 ± 0.36 1.11 ± 0.11 0.78 ± 0.45 1.38 ± 0.22 1.3 ± 0.58 0.79 ± 0.4 0.87 ± 0.53 0.81 ± 0.52 0.1646 0.9452 0.0319Coniochaeta 5.53 ± 0.54 4.06 ± 1.68 1.38 ± 0.62 4.22 ± 2.19 3.93 ± 0.94 2.04 ± 2.01 2.18 ± 0.37 2.04 ± 0.83 0.0373 0.6664 0.0116Coprinellus 1.44 ± 2.54 0.87 ± 1.41 0.05 ± 0.03 0.38 ± 0.63 0.23 ± 0.13 5.03 ± 10 0.04 ± 0.04 0.02 ± 0.01 0.0062 0.9535 0.7114Cryptococcus 3.71 ± 0.34 4.94 ± 1.03 4.47 ± 1.68 4.89 ± 0.76 3.86 ± 0.53 2.36 ± 1.13 2.57 ± 1.01 3.69 ± 0.23 0.7472 0.6525 0.1023Elasticomyces 2.05 ± 0.84 2.35 ± 0.38 7.51 ± 3.31 5.7 ± 2.28 3.08 ± 1.91 2.46 ± 2.32 4.49 ± 2.55 4.44 ± 2.57 0.0018* 0.464 0.9912Epicoccum 2.27 ± 0.83 2.82 ± 0.34 4.39 ± 0.83 2.45 ± 0.67 1.69 ± 0.58 1.16 ± 1 1.16 ± 0.41 1.2 ± 0.65 0.5471 0.1867 0.6686Fusarium 8.96 ± 6.68 9.35 ± 0.68 2.39 ± 1.29 5.45 ± 1.25 4.9 ± 0.52 6.04 ± 2.61 2.5 ± 1.69 5.17 ± 2.27 0.0002* 0.0028* 0.0332Glarea 0.81 ± 0.6 1.23 ± 0.57 2.14 ± 0.81 2.12 ± 0.99 6.44 ± 8.22 1.46 ± 1.44 1.97 ± 0.71 3.1 ± 1.89 0.1008 0.6478 0.2882Lophiostoma 0.43 ± 0.06 0.57 ± 0.07 0.43 ± 0.23 0.65 ± 0.22 0.83 ± 0.22 0.41 ± 0.21 0.54 ± 0.37 0.45 ± 0.16 0.5516 0.9113 0.1326Macroventuria 2.89 ± 1.07 3.9 ± 1.14 1.6 ± 0.88 2.31 ± 0.64 1.07 ± 0.31 0.72 ± 0.33 0.61 ± 0.35 0.66 ± 0.38 0.0044* 0.4976 0.2666Microdochium 0.72 ± 0.17 0.69 ± 0.24 0.16 ± 0.12 0.33 ± 0.15 1.19 ± 0.7 0.32 ± 0.13 0.36 ± 0.2 0.84 ± 0.66 0.0058 0.9333 0.0101Mortierella 1.17 ± 0.32 2.49 ± 0.4 0.4 ± 0.16 1.3 ± 0.58 2.08 ± 0.92 5.26 ± 3.97 1.23 ± 1.08 8.15 ± 4.32 0.0069 <0.0001* 0.0447Mycosphaerella 3.89 ± 4.14 3.02 ± 0.5 7.54 ± 3.86 3.26 ± 1.91 3.05 ± 0.63 2.17 ± 2.18 3.21 ± 1.29 3.3 ± 1.63 0.0922 0.1156 0.7573Phaeococcomyces 1.27 ± 0.71 1.52 ± 0.41 9.65 ± 3.4 5.67 ± 2.48 2.3 ± 0.88 1.02 ± 1.11 4.52 ± 1.66 3.83 ± 2.29 < 0.0001* 0.0737 0.8327Podospora 5.66 ± 1.45 6.3 ± 2.46 3.01 ± 4.26 8.9 ± 8.39 2.61 ± 2.6 0.76 ± 0.69 2.8 ± 3 0.6 ± 0.14 0.5652 0.646 0.2823Preussia 1.86 ± 0.5 1.97 ± 0.4 1.42 ± 0.73 1.64 ± 0.61 1.42 ± 0.43 0.75 ± 0.25 6.3 ± 6.21 7.66 ± 11.2 0.2224 0.5892 0.6597Trichoderma 1.65 ± 0.6 1.66 ± 0.56 0.46 ± 0.24 1.12 ± 0.59 1.56 ± 0.96 0.37 ± 0.29 0.54 ± 0.79 0.34 ± 0.35 0.0066 0.5827* 0.0243Ulocladium 3.37 ± 0.87 7.11 ± 1.51 2.45 ± 1.21 3.73 ± 0.79 5.81 ± 2.19 7.48 ± 4.96 3.17 ± 2.13 6 ± 1.16 0.0093 0.0037 0.4334

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3.5. Microbial OTUs of dust impacted by tillage and fertilizer treatment

Differential abundance analysis identified many OTUs that differedbetween tillage and fertilizer treatments. Among the fungi, OTUs re-lated to Pseudaleuria, Lasiosphaeriaceae, Neurospora terricola,Sordariomycetes and Mortierella were enriched in windblown dust afterbiosolids applications under both tillage regimes (Fig. 5). Similarly,Ceratobasidium (Rhizoctonia), Ascomycota spp, Dothideomycetes, andAuriculariales OTUs were more prevalent in dust under conservationtillage under both fertilizer treatments, versus conventional tillage.However, with synthetic fertilizer, a much larger number of OTUs re-lated Ascomycetous yeasts (Phaeococcomyces, Aureobasidium) weremore abundant with conservation tillage vs conventional tillage. In-terestingly, this pattern was not present after biosolids were applied,suggesting that the specific impacts of different tillage methods onfungal consortia in dust depend on the fertilization strategy.

Regardless of tillage treatment, fertilization with biosolids versussynthetic fertilizer enriched windblown dust for bacterial OTUs relatedto Bacillus, Janthinobacterium, Oxalobacteria, and Actinomycetes(Streptomyces, Actinomycetales) (Fig. 6). However, similar to fungalcommunities, the impact of tillage method on the prevalence of bac-terial OTUs depended on the fertilization method. Specifically, dustemitted from conservation tillage plots with synthetic fertilizer hadmany more OTUs that were enriched vs. conservation tillage plotsfertilized with biosolids. Dust from the synthetic fertilizer-conservationtillage treatment combination had higher relative abundances of manyProteobacteria and Bacteroidetes OTUs, including those related toPseudomonas, Sphingomonas, Rhizobium, Acetobacteriaceae, and Hyme-nobacter. In contrast, dust from the synthetic fertilizer-conventionaltillage treatment was enriched in many Actinobacterial OTUs, includingthose related to Streptomyces, Micromonospora, and Nocardioides(Fig. 6). Only a small number of Proteobacteria or Actinomycete OTUsdiffered between dust communities from the two tillage treatmentswhen plots were fertilized with biosolids.

3.6. Relationship between dust chemistry and microbial communities

Variation in windblown dust microbial communities was

significantly related to dust chemical characteristics (Figs. 3 and 4;Table 4). For fungal communities, Ni and Cr were the metals moststrongly correlated NMDS vectors (r2= 0.61 and 0.61, respectively;p < 0.0001), and tended to correspond with differentiation of fungalcommunities among sampling years. In contrast, concentrations of P,the C:N ratio, and many trace metals were more strongly related todifferentiation of dust communities between conventional tilled versusconservation tilled plots (Fig. 3; Table 4). Similar relationships werefound between Ni and Cr concentrations in dust and separation ofbacterial communities between sampling years (Fig. 4; Table 4).However, there was no significant relationship between the C:N ratioand many other trace metals with NMDS vectors. Thus, variation in dustchemistry, especially Ni and Cr, is related to dust microbial communitycomposition.

4. Discussion

Microbes are ubiquitous components of our atmosphere, where theyplay crucial roles in ecosystem processes, as well as plant and humanhealth as pathogens and allergens (Barberán et al., 2015; Després et al.,2012; Fröhlich-Nowoisky et al., 2016; Gandolfi et al., 2013; Gardneret al., 2012; Katra et al., 2017; Palmero et al., 2011). Erosion of soilsduring high wind events is hypothesized to be a major contributor tothe microbial loading of airborne particulates from agricultural fieldsand has important implications for soil and human health, as well as thedispersal and biogeography of microbial populations (Acosta-Martínezet al., 2015; Barberán et al., 2015). Consistent with the strong potentialof microbial communities on the soil surface to become aerosolized, thecomposition of fungal and bacterial communities in fine dust emittedfrom tilled fallow fields during simulated high wind events broadlyreflected those phyla commonly found in soil, including many Asco-mycota, Actinobacteria, Proteobacteria, and Bacteroidetes, (Janssen,2006). However, both tillage (conventional vs. conservation) and fer-tilization (synthetic vs. biosolids) practices significantly impacted thecomposition of dust microbial communities, suggesting that field-scalemanagement decisions modify the microbial composition of windblowndust. This implies that adoption of conservation tillage and biosolidapplications in the PNW will not only reduce erosion, but modify the

Table 3Mean percentage (± standard deviation) of abundant bacterial genera in dust. ANOVA p-values marked with * are significant after an FDR correction (FDRp < 0.1).

Genus 2015 2016 ANOVA p-value

Conventional Conservation Conventional Conservation

Fert Bio Fert Bio Fert Bio Fert Bio Tillage Fertilizer Tillage xFertilizer

Amycolatopsis 0.57 ± 0.21 2.16 ± 1.37 0.96 ± 0.13 2.06 ± 0.7 0.82 ± 0.27 1.72 ± 0.64 0.54 ± 0.25 1.56 ± 0.25 0.709 0.478 0.087Bradyrhizobium 1.23 ± 0.28 0.76 ± 0.36 0.92 ± 0.17 0.81 ± 0.34 1.48 ± 0.06 1.04 ± 0.19 0.73 ± 0.17 0.91 ± 0.15 0.059 0.352 0.161Burkholderia 0.85 ± 0.34 1.17 ± 0.69 1.01 ± 0.53 0.79 ± 0.25 1.07 ± 0.16 0.8 ± 0.01 0.45 ± 0.16 0.74 ± 0.19 0.08 0.189 0.143Candidatus Solibacter 0.87 ± 0.19 0.28 ± 0.12 0.54 ± 0.11 0.29 ± 0.1 0.69 ± 0.06 0.42 ± 0.09 0.61 ± 0.15 0.28 ± 0.11 0.083 0.51 0.726DA101 1.7 ± 0.54 0.63 ± 0.16 1.44 ± 0.48 0.57 ± 0.05 1.51 ± 0.18 0.89 ± 0.2 1.66 ± 0.41 0.83 ± 0.05 0.595 0.047 0.527Friedmanniella 0.68 ± 0.06 0.91 ± 0.65 0.95 ± 0.07 1.16 ± 0.48 0.62 ± 0.15 1.01 ± 0.13 1.1 ± 0.28 1.14 ± 0.15 0.007 0.515 0.961Gemmata 1.08 ± 0.4 0.13 ± 0.07 0.72 ± 0.18 0.16 ± 0.07 0.96 ± 0.39 0.25 ± 0.18 1.19 ± 0.28 0.17 ± 0.06 0.995 0.121 0.737Hymenobacter 1.27 ± 0.43 0.56 ± 0.52 1.37 ± 0.16 0.71 ± 0.42 0.97 ± 0.07 0.25 ± 0.18 3.04 ± 0.39 0.62 ± 0.24 0.005* 0.948 0.038Janthinobacterium 0.78 ± 0.15 1.9 ± 1 0.72 ± 0.18 1.89 ± 0.94 0.6 ± 0.37 0.57 ± 0.11 0.54 ± 0.33 0.61 ± 0.29 0.559 0.001* 0.929Kaistobacter 2.17 ± 0.21 1.83 ± 0.43 1.73 ± 0.33 2.01 ± 0.6 2.22 ± 0.55 1.9 ± 0.16 1.17 ± 0.21 1.66 ± 0.15 0.007 0.125 0.059Kribbella 0.73 ± 0.28 1.68 ± 0.86 0.58 ± 0.28 1.14 ± 0.47 0.6 ± 0.18 1.1 ± 0.35 0.43 ± 0.11 1.1 ± 0.1 0.113 0.207 0.672Methylobacterium 0.69 ± 0.17 1.03 ± 0.41 1.09 ± 0.05 1.26 ± 0.37 0.66 ± 0.11 0.62 ± 0.1 1.13 ± 0.31 0.92 ± 0.25 0.001* 0.078 0.574Modestobacter 1.51 ± 0.1 2.49 ± 0.78 2.09 ± 0.31 2.99 ± 0.74 1.58 ± 0.2 2.79 ± 0.32 2.05 ± 0.13 3.22 ± 0.18 0.001* 0.325 0.615Mycobacterium 0.33 ± 0.06 0.67 ± 0.23 0.42 ± 0.11 0.73 ± 0.35 0.45 ± 0.02 0.91 ± 0.14 0.41 ± 0.13 1.02 ± 0.26 0.436 0.042 0.408Nocardioides 0.48 ± 0.1 0.75 ± 0.17 0.46 ± 0.1 0.79 ± 0.33 0.57 ± 0.12 0.83 ± 0.16 0.29 ± 0.08 0.73 ± 0.18 0.046 0.419 0.042Pedobacter 1.67 ± 1.9 1.01 ± 0.81 0.66 ± 0.49 0.55 ± 0.31 1.13 ± 0.63 0.12 ± 0.05 0.93 ± 0.35 0.2 ± 0.1 0.309 0.093 0.167Pseudonocardia 0.75 ± 0.18 0.95 ± 0.2 0.64 ± 0.21 1 ± 0.12 0.77 ± 0.17 1.55 ± 0.41 0.5 ± 0.15 1.23 ± 0.08 0.085 0.367 0.13Segetibacter 5.12 ± 1.05 2.55 ± 1.28 4.8 ± 0.82 2.73 ± 0.6 3.96 ± 0.11 1.92 ± 0.37 6.06 ± 0.94 2.36 ± 0.43 0.093 0.406 0.1Sphingomonas 0.43 ± 0.16 0.99 ± 0.39 0.73 ± 0.28 1.06 ± 0.6 0.63 ± 0.04 0.32 ± 0.06 1.36 ± 1.28 0.52 ± 0.14 0.134 0.392 0.525Streptomyces 0.41 ± 0.15 1.54 ± 0.56 0.19 ± 0.08 0.81 ± 0.32 0.57 ± 0.51 0.58 ± 0.08 0.09 ± 0.01 0.4 ± 0.21 0* 0.006 0.419

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composition of microbial communities that characterize dust emittedfrom eroding agricultural fields.

Conservation tillage, such as through the use of an undercutter ra-ther than conventional disk tillage, is one of the primary means of re-ducing wind erosion in the PNW wheat-fallow cropping region(Papendick, 2004) and reduced soil loss by 12–61% throughout thecourse of this experiment (Pi et al., 2018b). Moreover, by limiting soildisturbance and leaving plant residue on the soil surface, rather thanburying residue and making it available for decomposition, conserva-tion tillage promoted microbial taxa that proliferate on surface residue.Notably, dust emitted from plots managed with conservation tillage hadrelatively lower abundances of Ascomycota but greater abundances ofunidentified fungi than dust emitted from conventional disk tillageplots. Members of the Ascomycota that were less prevalent in dustemitted with conservation tillage included the genera Chaetomidium,Chaetomium, Fusarium, Macroventuria, Microdochium, Mortierella, Tri-choderma, and Ulocladium. These groups, especially Chaetomium, Ulo-cladium, and Fusarium, are among the most common fungal taxa in soilsin wheat-based cropping systems in this region (Sharma-Poudyal et al.,2017). These genera are likely decomposers of wheat residue in soil thatare enriched in windblown dust when conventional disk tillage buriesresidues below ground where conditions are more favorable for de-composition, and concurrently moves propagules from deeper soillayers to the soil surface where they are subject to erosion. Fusarium cancause allergic reactions (Hoff et al., 2003), as well as Chaetomium

(Andersen et al., 2011). Moreover, in addition to saprophytes, the genusFusarium also contains numerous plant pathogenic species, some ofwhich produce mycotoxins that are harmful to animals and humans(Antonissen et al., 2014; Paulitz, 2006).

Among bacterial taxa in dust that were influenced by tillage, thephylum Nitrospiraceae tended to be less abundant in dust emitted fromplots under conservation tillage. As nitrite-oxidizers, members of thisphylum are critical components of the nitrogen cycle (Garrity et al.,2001). Further, members of genera Nocardioides and Streptomyces, andother members of Actinobacteria that are often involved in degradationof complex substrates in soils (Lewin et al., 2016) tended to be lessabundant in dust emitted from conservation tillage. Although thesegroups play crucial roles in nutrient cycling and plant disease sup-pression in soils (Kinkel et al., 2012; Schlatter et al., 2008), they canalso be important allergens (Johansson et al., 2011). Kaistobacter (fa-mily Sphingomonadaceae), is also a common inhabitant of soils in thisregion and can be transported on intercontinental dust (Favet et al.,2013). Thus, conservation tillage versus conventional tillage both re-duced the dispersal of many important allergenic and potentially pa-thogenic fungal and bacterial taxa in windblown dust, while limitingthe loss of those involved in decomposition of crop residues and nu-trient cycling.

In contrast to taxa that thrive on buried residue, dust emitted fromplots managed with conservation tillage had greater portions taxa thatlikely proliferate or survive on residue left on the soil surface. Examples

Fig. 5. Differentially abundant fungal OTUs between fertilizer (upper) or tillage (lower) treatments within each tillage (conventional or conservation) or fertilizer(biosolids or synthetic fertilizer) treatment. Each point represents an OTU with the x-axis representing the estimated log2-fold difference in abundance betweentreatments. The size of points are scaled by the mean abundance of that OTU among all samples (baseMean) and points are colored by the phylum to which theybelong. Labels on the y-axis indicate the best BLAST hit to the UNITE ITS database, followed by the % similarity and OTU identifier.

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were the fungal genera Cadophora (Phialophora), Elasticomyces, andPhaeococcomyces (Exophiala). Cadophora and Phaeococcomyces areamong the most abundant inhabitants of wheat roots and proliferateafter plant death (Schlatter et al., 2018). Thus, these groups are likelypresent as wheat endophytes, build up on surface residue after plantdeath, and then aerially disperse during high wind events. Notably,although common in outdoor- and indoor-environments, Cadophoraand Phaeococcomyces (black yeast-like fungi) are potentially importantallergens contributing to lung infection and asthma (Revankar andSutton, 2010). These fungal taxa are also well-adapted to harsh en-vironmental conditions and form hardy, melanized, spore structures,suggesting that they will remain viable during atmospheric dispersal.Fungal communities in dust emitted from plots where conservationtillage was used also had greater frequencies of some individual OTUsthat are likely to represent plant pathogenic taxa, such as Cer-atobasidium (Rhizoctonia), which causes significant root disease in no-till systems (Paulitz, 2006).

Many of the bacterial genera enriched in dust emitted from con-servation tillage (Fridemanniella, Modestobacter, Hymenobacter) are alsogroups that are especially well adapted to survive harsh environments.For example, Modestobacter species produce melanin under nutrient-poor conditions, such as those that would be expected on surface re-sidue (Reddy et al., 2007). Moreover, Methylobacterium, an importantbeneficial plant symbiont that feeds on methanol released from plant

Fig. 6. Differentially abundant bacterial OTUs between fertilizer (upper) or tillage (lower) treatments within each tillage (conventional or conservation) or fertilizer(biosolids or synthetic fertilizer) treatment. Each point represents an OTU with the x-axis representing the estimated log2-fold difference in abundance betweentreatments. The size of points are scaled by the mean abundance of that OTU among all samples (baseMean) and points are colored by the phylum to which theybelong. Labels on the y-axis indicate the lowest level of taxonomy to which an OTU could be identified with confidence (80%) using the RDP classifier, followed bythe OTU identifier. g= genus, f = family, o= order, p=phylum.

Table 4Correlations of dust chemistry fitted to microbial NMDS ordinations. P-valuesare based on 1 000 permutations.

Fungi Bacteria

R pvals R pvals

Ca 0.39 0 0.19 0.09Fe 0.43 0 0.13 0.18K 0.26 0.01 0.18 0.1Mg 0.49 0 0.23 0.05P 0.45 0 0.18 0.11Ba 0.28 0.01 0.23 0.05Cr 0.61 0 0.45 0Co 0.39 0 0.13 0.21Cu 0.26 0.02 0.02 0.82Pb 0.28 0.02 0.26 0.02Mn 0.41 0 0.1 0.3Mo 0.36 0 0.21 0.04Ni 0.61 0 0.48 0V 0.31 0 0.06 0.46Zn 0.03 0.63 0.14 0.17C 0.01 0.87 0.03 0.65N 0.16 0.09 0.13 0.2C:N 0.32 0 0.17 0.11

Bold numbers are P values ≤ 0.05.

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stomata (Peyraud et al., 2012), also tended to be enriched with con-servation tillage. Most of the individual OTUs that were more abundantin dust emitted from conservation tillage plots belonged to the phylumProteobacteria, and included taxa related to Acetobacteraceae, Sphin-gomonadaceae, Sphingobacteraceae, Rhizobium, and Pseudomonas.These groups are frequently associated with wheat roots and are likelyinvolved in cellulose degradation of wheat straw (Tardy et al., 2015;Zhou et al., 2016). Thus, conservation versus conventional tillagechanged the spectrum of dust-associated microbes aerosolized duringhigh-wind events. Moreover, conservation tillage reduced the dispersalof many soil taxa important in decomposition and nutrient cycling insoil in favor of plant-associated taxa that can survive or proliferate onplant residue on the soil surface.

In addition to conservation tillage, the use of biosolids as a soil sta-bilizer may further modify the microbial component of dust emitted fromagricultural fields via reduction in erosion potential or effects on soil- orplant-associated microbial communities. Further, class B biosolids, whichare most commonly applied to agricultural fields, may directly impactmicrobial communities through introduction of exogenous microbes.However, our results suggest that shifts in dust-associated fungal com-munities are likely due to increases in indigenous, saprotrophic taxa thatfeed on biosolids material. For example, dust emitted from biosolids-amended plots had significantly greater proportions of Zygomycota, lar-gely due to a single OTU related toMortierella, which is a common, rapidlygrowing, soil fungus. Interestingly, the specific fungal genera increased indust by biosolids amendments showed considerable overlap with thosedepleted via the use of conservation tillage. For example, Fusarium,Mortierella, and Ulocladium, which are likely feeding on crop residues thatare tilled into the soil with disking, were also enriched with biosolidsamendments, suggesting a core group of copiotrophic fungi with broadresource use niche (Essarioui et al., 2016) respond similarly to differentorganic inputs. In related work, OTUs belonging toMorteriella, Neurospora,Fusarium, and Ulocladium were the primary colonizers of biosolids ag-gregates in soil and were highly enriched after biosolids amendments(Schlatter et al., 2017), suggesting that these resident soil fungi grow onbiosolid material and are then transported on dust during high-windevents.

Bacteria are highly abundant in municipal sewage and present atsignificant levels in Class B biosolids after anaerobic digestion.Moreover, because biosolids-inhabiting bacteria can include potentialhuman pathogens, they are considered a risk to public health whenaerosolized (Brooks et al., 2012). Although bacterial phyla (Firmicutesand Proteobacteria) that make up substantial components of the humangut microbiome (Shreiner et al., 2015) were more prevalent in dustfrom biosolids-amended fields, the genera (Janthinobacterium, Myco-bacterium, and Streptomyces) and OTUs (Oxalobacteriaceae and Bacillus)that were consistently enriched represent common soil bacteria. Thus,similar to fungi, the bacteria enriched in dust from biosolids-amendedfields likely represent copiotrophic, soil-dwelling species that feed onintroduced nutrients, whereas those from synthetically-fertilized fields(eg. DA101 [phylum Verrucomicrobia]) are oligotrophs that grow inrelatively carbon-poor conditions (Fierer et al., 2007).

Members of the family Clostridiaceae (phylum Firmicutes), al-though of relatively low abundance in windblown dust, as a whole,were higher in dust from the biosolids-treated plots. Clostridium spp. arepresent in soils, but are one of the most abundant groups in healthyhuman gut flora (Lopetuso et al., 2013). Thermotolerant Clostridia,which are selected for in biosolids during anaerobic digestion of sewageand survive in soil as recalcitrant endospores, have been suggested as asensitive indicator of aerosolized enteric pathogens after biosolids ap-plications (Dowd et al., 1997; Brooks et al., 2007). Other species ofClostridium, such as C. difficile, C. perfringens, and C. botulinum, areimportant pathogens themselves, but species-level identification fromshort 16S rRNA sequences is unreliable. Further, it remains unclear ifthe Clostridium DNA detected originated from viable or dead cells.Nevertheless, there is significant potential for Clostridium to be

aerosolized during wind erosion events after biosolids applications.Thus, although most bioaerosols originate from soil (Brooks et al.,2007), high wind events have the potential to aerosolize biosolid-de-rived microbes (Baertsch et al., 2007). Notably, other enteric bacteriaand human-associated fungi were not found in windblown dust, and arelikely susceptible to die off in soil because of high temperatures, limitedmoisture, lack of resistant spore structures, and competition with in-digenous soil microbes.

At broad geographic scales, pH has been suggested to be a strongpredictor of microbial communities in bioaerosols, likely due to itssignificant role in structuring soil communities (Barberán et al., 2015;Lauber et al., 2009). However, within plots at the Lind site, the C:Nratio and metal concentrations were among the best predictors of fungalcommunities in windblown dust, whereas metals (Ni, Cr, Pb, Mg, Ba)were more associated with differences in bacterial communities, in-dicating distinct chemical correlates at localized versus broad geo-graphic scales. This reflects patterns observed with soil communities(Schlatter et al., 2017), suggesting that variation in the chemicalproperties of windblown dust significantly associated with microbialcommunities are likely due to shifts in the C:N ratio and introduction oftrace metals to soil via different tillage and fertilizer treatments (Piet al., 2018a).

Because aerosolized communities in outdoor environments are im-portant sources of those found within building environments (Adamset al., 2013, 2016; Burge, 2002), aerosolization of microbial popula-tions from agricultural systems has important implications for humanhealth, especially in communities dominated by agricultural land-scapes. Soilborne plant pathogens that form resistant survival structurescan also be spread by windblown dust (Rennie et al., 2015). Thus, thespecific tillage and fertilizer strategies employed at a regional scale arelikely to impact indoor microbiomes (Leung and Lee, 2016; Weikl et al.,2016). More data on seasonal variation in aerosolized communitiesfrom different cropping systems along with monitoring of microbiomesof adjacent indoor microbiomes will shed important light on the lin-kages between cropping practices and environmental health of indoorenvironments.

This work demonstrated that tillage and fertilization practices aresignificant drivers of microbial communities that are aerosolized withdust emitted from dryland cropping systems during high wind events ineastern Washington. Although microbial communities of windblowndust were dominated by soil taxa, tillage practice had a significant ef-fect on airborne microbiota. Indeed, the airborne microbiota associatedwith conservation tillage was characterized by communities associatedwith colonization and decomposition of plant residue on the soil surfaceversus those that degrade plant material in soil. Further, the use ofbiosolids versus synthetic fertilizers enriched windblown dust primarilywith copiotrophic taxa able to consume introduced nutrients, thoughsome endospore-forming human-associated bacteria (Clostridia) mayalso be present. Thus, the impacts of cropping practices on the airbornemicrobiome at a regional scale should be considered in the context ofplant and human health.

Acknowledgements

The authors thank John Jacobsen and Bruce Sauer for their ex-cellent assistance in the field management of this experiment. Fundingfor the research was provided by the USDA-Agricultural ResearchService, Washington State University, King County, WashingtonDepartment of Natural Resources, and Northwest Biosolids. The firstauthor was funded by Administrator funded Postdoctoral ResearchAssociate award from the USDA-Agricultural Research Service.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.atmosenv.2018.04.030.

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