By 2009, the farm labor force had few SAWS . Thus, to maintain a large and flexible agricultural worker force, a steady stream of new, young workers is required— whether it be from a porous border, temporary work permits, or a perpetual program of earned legalization through farm work.Multiple aspects of intensive agriculture adversely alter AMF communities. For example, intensively tilled agricultural soils tend to select for a less diverse, more ruderal AMF community, which includes taxa thought to have fewer mutualistic traits . Intensive tillage and bare fallows also decrease AMF colonization of crops by disrupting hyphal networks and leaving AMF without hosts, respectively . Heavy fertilization can create less mutualistic and abundant AMF associations and suppress colonization of roots by AMF. Specifically, high nutrient availability via fertilization decreases the dependency of plant hosts on AMF but selects for AMF that are more aggressive competitors for plant carbohydrates, leading to a net cost to plant hosts . Farm management also influences AMF community composition indirectly via changes in other soil properties, such as soil pH and soil organic carbon . For example, adding fertilizer acidifies soils and tilling reduces soil organic carbon, both of which can drive changes in AMF communities . Apart from tillage, fertilization and other practices that change soil properties, agricultural soils may also have low numbers of AMF taxa because of the extremely low diversity of plant hosts when crops consist of mono cultures in space and/or over time . By contrast, poly cultures, which are more similar to bio-diverse natural systems, have the potential to positively impact AMF communities by providing a more diverse set of plant hosts. While agriculture continues to shift towards mono cultures, poly cultures have traditionally been the dominant form of agriculture across many regions in the world and have been promoted as a way to remedy the negative environmental impacts that intensive monoculture agriculture has had on soils , especially through AMF associations . Yet surprisingly little is known about how AMF respond to poly cultures . More generally, the underlying mechanism driving the relationship between AMF and plant diversity in managed or natural ecosystems has not been fully resolved. Observational studies in natural ecosystems cannot differentiate whether AMF diversity supports greater plant diversity or AMF diversity is dependent on plant composition .

While AMF are generalists, there is a degree of selectivity in AMF associations with plant hosts ,vertical farming equipments and thus plant hosts could filter AMF communities directly . Alternatively, plant hosts could impart changes in the soil environment that alter AMF communities indirectly . Because the suite of plant hosts is carefully managed in agricultural systems, studies in agroecosystems might help to clarify the mechanism underlying the relationship between plant diversity and AMF communities. Therefore, we sought to understand how AMF communities respond to greater crop diversity in an intensively managed agricultural landscape. Our study compared AMF communities in soil, the legacy of previous management, as well as AMF colonization in roots in monoculture vs poly culture fields. Our experimental design allowed us to investigate the filtering effect of crop diversity on AMF communities, as well as how AMF communities could be influenced by soil properties. To reduce confounding effects of management practices on AMF, all field sites studied used similar tillage regimes and fertilizers, allowing us to focus on management differences in crop diversity . Specifically, we asked: how does greater crop diversity via poly culture and its management legacy affect AMF richness and diversity relative to monoculture cropping; to what extent does AMF community composition differ between poly culture and monoculture field sites; how does AMF root colonization differ between crop plants grown in monoculture vs poly culture field sites; and, ultimately, do soil properties impact AMF community composition and colonization? We predicted that despite the legacy of intensive agricultural practices on soils in our study region, greater crop diversity would have a positive effect on richness and diversity. We also expected that the more diverse plant community in poly cultures may foster more beneficial, host-specific AMF taxa, which, in turn, lead to greater AMF root colonization for a given crop host when planted in poly culture rather than monoculture fields . Based on previous literature, we also anticipated that soil properties would have an overriding effect on AMF community composition, but that crop diversity still plays an important role in shifting the AMF community. In this way, our study aims to increase our understanding of the relationship between plant diversity and AMF communities and to investigate whether greater crop diversity could support more sustainable agriculture systems via AMF communities.

Farm sites were located in Fresno County in California’s San Joaquin Valley, an agriculturally dominated region containing a wide range of annual and perennial crops, including row crops and orchards. While this region is dominated by large-scale farms, our research focused on small-scale farms embedded in this landscape. Some of these small-scale farms grow a high diversity of specialty crops together, such as bok choy , chard , Thai peppers , jujube and bitter melon . Farmers frequently rotate these crops over space and time in previously intensively mono cropped farmed land. Specifically, on poly culture farms, different crop types are planted in close proximity alternating as little as every couple of rows.In order to compare monoculture and poly culture management and understand the effect of plant hosts on AMF communities, we selected farms if they grew at least two rows of the same ‘focal’ crop: a summer squash variety or eggplant , which both associate with AMF . In addition, all farms used conventional tillage and cultivation to prepare beds and synthetic inputs to some extent but no soil fumigants. Farms were considered poly cultures if they grew 20 or more different crop types and monoculture if they grew one crop type at the time of sample collection. The poly culture farms had a range of 7 to over 15 yr in this management whereas monoculture farms, except for one, had been in monoculture management for over 15 yr. Information on site history is provided in Supporting Information Table S1. We sampled 25 farms during autumn 2017 and early summer 2018 . Some eggplant farms were sampled in both years while others were sampled in just one year , corresponding to peak productivity and the end of the eggplant growing season in this region. All squash farms, which have a shorter growing season, were only sampled in summer 2018. In total, we sampled 31 separate sampling units across both years : 11 eggplant poly cultures, nine eggplant mono cultures, five squash poly cultures and five squash mono cultures .We used the same sampling scheme across all monoculture and poly culture field sites . We set up two sets of 30 m transects arranged: within-row and across-row . Within-row transects ran along rows of the focal crop , with across-row transects running perpendicular. For each transect, sampling occurred at three points 10 m apart for a total of three sampling points per transect and 12 sampling points per field site. In total, we collected 372 samples across 31 field sites.Our sampling scheme was designed to compare AMF community composition on the same crop host whether it was grown in a poly culture or monoculture management through the within-row transects. We localized our within-row transects on eggplant or squash to limit plant species and varietal variation for plant host effects on AMF community composition.

This design also allowed us to investigate how the interaction between farm management and different crop hosts would impact the AMF community. On poly culture fields, across-row transects would intersect crop hosts distinct from the focal crop, whereas on monoculture fields, these transects would still intersect the focal crop . In this way, we were able to investigate whether the legacy of farm management on each field site was sustained across different crop hosts.Following the sampling scheme described earlier , we collected root and soil samples to characterize the AMF community on the focal crops, squash and eggplant, and on other crops . We collected a soil sample from the root zone using an auger , avoiding areas where weeds were present. We separated roots from each soil sample to determine mycorrhizal colonization. However, we were unable to collect enough roots for molecular characterization; we were limited by our sampling because we sampled on working farms not experimental fields and thus were unable to attain farmer permission to harvest whole plants. The rest of the soil sample was divided into a sub-sample, with all visible roots and rocks removed, for molecular measurements of AMF communities and a sub-sample for measuring soil properties.Soil samples for edaphic measurements were air-dried and sieved in a 2 mm sieve. To determine total organic carbon , nitrogen content , and C : N each soil sample was ground and assessed using a combustion elemental analyzer,vertical farms which is able to separate organic carbon from inorganic carbon . Soil texture for each sample was determined using the ‘micropipette’ method . The remaining soil chemical properties were measured at the University of Massachusetts Soil and Plant Testing Facility . Soil chemistry data included pH, cation exchange capacity , and mg kg 1 of total extractable phosphorus , potassium , calcium , magnesium , zinc , boron , manganese , copper , iron , lead , aluminium , sodium and sulfur . A ‘soil properties index’ was created using the first principal component scores of the principal component analysis of all the edaphic measurement data. The PC1 axis explained 27.9% of the variation and had a negative loading for percentage sand and a positive loading for Ca . The PC2 axis explained 15.5% variation and had a negative loading for Mg and a positive loading for N .We determined the percentage colonization by counting AMF composition in stained roots. Roots were cleared in 10% KOH, acidified in 1% HCl and stained with trypan blue . Percentage colonization by AMF was determined using the intersections method at 9200 magnification . Arbuscular mycorrhizal fungi colonization in this study refers to percentage colonization by arbuscules, vesicles or hyphae over total intersections counted .Soil samples for molecular measurements were immediately stored at 80°C upon return to the laboratory until DNA extractions could proceed.

DNA was extracted from 0.25 g of soil using the DNeasy PowerSoil Kit . Detailed information about the molecular analysis, specifically primer selection, PCR conditions and amplicon library preparation, can be found in the Methods S1. Briefly, the ITS2 rRNA region was amplified to characterize the communities of fungi. In previous studies, ITS2 primers have also matched well with all lineages in Glomeromycotina and, in the same study region, they have been successfully used to study fine-scale patterns of AMF community succession . The forward and reverse primers contained a 29 or 25 base linker, a 12 base barcode, a 29 or 34 base pad, and a 0–8 base heterogeneity spacer . Sequencing of amplicon libraries was performed on the Illumina MiSeq platform with 300 bp paired-end reads at the Vincent J. Coates Genomics Sequencing Laboratory . The AMPtk pipeline was used to process the fungal sequence data . First, the forward and reverse sequences were demultiplexed and the primers were removed. Then, sequences were denoised into exact sequence variants using the UNOISE3 algorithm, which removes artificial sequences including predicted sequence errors, contaminants such as putative PhiX carry-over from Illumina sequencing, and putative chimeric sequences . Next, the resulting exact sequence variants sequences were clustered in biologically relevant operational taxonomic units at 97% sequence similarity using the UCLUST algorithm employed in VSEARCH . Then to account for index bleed , the synthetic mock community was also used to calculate the observed rates of index bleed to remove spurious OTUs using the filter module in the AMPtk pipeline . DNA extraction and PCR-negative controls sequence reads present in samples were also subtracted from each sample . Finally, taxonomy was assigned using the AMPtk ‘last common ancestor’ approach with the combination of global sequence, UTAX, and SINTAX alignments against the UNITE v.7.2.2 database . The resulting OTU table contained 243 AMF OTUs and 3175 non AMF fungal OTUs . Raw sequence read files are available in NCBI SRA accession PRJNA650414.