DOI: 10.5061/dryad.q2bvq83g1
title: Data from: Contrasting patterns of functional diversity in coffee root fungal communities associated with organic and conventionally-managed fields

description: The structure and function of fungal communities in the coffee rhizosphere
 is shaped by crop environment. Because coffee can be grown along a
 management continuum from conventional application of pesticides and
 fertilizers in full sun to organic management in a shaded understory, we
 used coffee fields to hold host constant while comparing rhizosphere
 fungal communities in markedly different environmental conditions with
 regard to shade and inputs. We characterized the shade and soil
 environment in 25 fields under conventional, organic or transitional
 management in two regions of Costa Rica. We amplified the ITS2 region of
 fungal DNA from coffee roots in these fields and characterized the
 rhizosphere fungal community via high-throughput sequencing. Sequences
 were assigned to guilds to determine differences in functional diversity
 and trophic structure among coffee field environments. Organic fields had
 more shade, a greater richness of shade tree species, more leaf litter,
 and were less acidic, with lower soil nitrate availability and higher soil
 copper, calcium, and magnesium than conventionally-managed fields,
 although differences between organic and conventionally-managed fields in
 shade, calcium and magnesium depended on region. Differences in richness
 and community composition of rhizosphere fungi between organic and
 conventionally-managed fields were also correlated with shade, soil
 acidity, nitrate, and copper. Trophic structure differed with coffee field
 management. Saprotrophs, plant pathogens, and mycoparasites were more
 diverse and plant pathogens were more abundant in organic than in
 conventionally-managed fields, while saprotroph-plant pathogens were more
 abundant in conventionally-managed fields. These differences reflected
 environmental differences and depended on region. IMPORTANCE Rhizosphere
 fungi play key roles in ecosystems, as nutrient cyclers, pathogens, and
 mutualists, yet little is currently known about which environmental
 factors and how agricultural management shape rhizosphere fungal
 communities and their functional diversity. This field study of the coffee
 agroecosystem suggests that organic management not only fosters a greater
 overall diversity of fungi, but also maintains a greater richness of
 saprotrophic, plant pathogenic and mycoparasitic fungi that has
 implications for efficiency of nutrient cycling and regulation of plant
 pathogen populations in agricultural systems. As well as influencing
 community composition and richness of rhizosphere fungi, shade management
 and use of fungicides and synthetic fertilizers altered the trophic
 structure of the coffee agroecosystem.
description: <b>Site
 </b><b>description</b><b> and
 </b><b>study design.</b> Two coffee-growing regions of
 Costa Rica with a premontane wet forest climate were selected for this
 study, Monteverde (10⁰ 19'27.8" N, 084⁰ 50'30.1" W)
 and San Vito (08⁰ 52'41.1" N, 082⁰ 57'03.1" W). Soils
 in both Monteverde and San Vito are Andisols, a volcanic soil type with
 high organic matter, high leaching capacity and pH of 5.6 - 5.8.
 Monteverde experiences slightly lower rainfall on average (300 cm
 yr<sup>-1</sup> vs. 400 cm yr<sup>-1</sup> in San
 Vito. Twenty-five
 coffee fields were included in this study. Thirteen fields were sampled in
 Monteverde, six between 25-28 May 2011 and seven between 1-4 June 2012. In
 San Vito, six fields each were sampled between 31 May-3 June 2011 and 7-11
 June 2012. At each site, the farmer or farm manager was interviewed to
 determine types of herbicides, pesticides, fungicides, and fertilizers
 used on the field, as well as the cultivars present, age of the field and
 coffee plants, prior land use and pruning regimen. Fields were designated
 as ‘conventionally-managed’ if farmers reported using synthetic
 fertilizers and pesticides, as ‘organic’ if farmers reported that fields
 were certified organic or reported no use of synthetic fertilizers and
 pesticides in the previous five years, and as ‘minimal conventional’ if
 farmers reported that they were in the process of transitioning from
 conventional to organic management or had not used synthetic fertilizers
 or pesticides in the preceding 1-3 years. <b>Field
 </b><b>sampling</b>. For each field, species richness of
 shade trees, type of windbreak, and phenological status of coffee plants
 (vegetative, flowering, green or mature fruit) were recorded. All fields
 except one, in which plants were vegetative, were producing green
 (immature) or green and red (mature) fruits at the time of sampling. In
 each field, a 20 m × 20 m plot was established &gt; 5 m from the edge
 and representative of the shade tree density of the field. Approximate
 elevation was recorded with a Garmin eTrex Venture HC® (Garmin Corp.,
 Schaffhausen, Switzerland). Percent canopy cover at the center of the plot
 was calculated using a spherical densiometer with convex mirror (Forestry
 Suppliers, Jackson, Mississippi, USA) according to manufacturer’s
 instructions. Plot aspect was measured by compass; plot slope was measured
 qualitatively in 2011 and using a clinometer in 2012. Coffee plant density
 was estimated by averaging the distance between rows for five rows and the
 distance between plants within a row for five pairs of plants.
 Within each plot, one coffee
 plant was sampled every 5 m along every other row, for a total of 20
 plants per plot. At each plant, leaf litter depth was measured at the
 dripline, and a soil sample was taken using a 2 cm in diameter corer to a
 depth of approximately 20 cm. From every other sampled plant, root samples
 were taken at 1-15 cm of depth from 3-5 sections of fine roots and
 combined, for a total of 10 plants per plot. Soil samples within a field
 were pooled, air-dried in paper bags and stored at room
 temperature. In
 the lab, each root sample was rinsed with tap water and divided in two.
 One subsample from each plant was stored in 1% KOH
 (<i>w/v</i>) for analysis of root colonization by AM fungi
 (Aldrich-Wolfe et al., in review), while the second was dried in the
 presence of Drierite (W.A. Hammond Company, Xenia, Ohio, USA) for DNA
 extraction. Drying roots results in no reduction in DNA yield relative to
 isolation from fresh or frozen samples, although it may reduce the yield
 of fungal DNA (86), and eliminates the risk of DNA degradation when frozen
 samples thaw in transit (87). At the end of each year’s sampling period,
 soils and dried root samples for DNA extraction were transported to the
 United States and stored at room temperature. Two-three soil subsamples
 from each field were analyzed for soil nutrient availability, pH in water,
 and organic matter by LOI at the Soils Testing Laboratory, North Dakota
 State University, Fargo, North Dakota, USA. Means per field were
 subsequently used for all statistical analyses. <b>Molecular detection of
 root fungi</b><b>.</b><i> </i>Dried root
 samples were pulverized using six 2.33-mm in diameter chrome-steel beads
 (Biospec Products, Bartlesville, Oklahoma, USA) in a vortex adapter (Mo
 Bio Laboratories, Carlsbad, California, USA) on a Vortex-Genie® 2 Mixer
 for 1 h (Scientific Industries, Inc., Bohemia, New York, USA). DNA was
 isolated from 20 mg of each sample for 8-10 root samples per field using
 the Qiagen DNeasy Plant Mini Kit (Qiagen, Germantown, Maryland, USA),
 following the manufacturer’s protocol (with two elution volumes of 50 μL
 each) and stored at -20 °C. The internal transcribed spacer
 region 2 (ITS2) was amplified by polymerase chain reaction (PCR) for each
 DNA extract using 12.5 μL of 2× Kapa HiFi Hotstart Ready Mix (Kapa
 Biosystems, Wilmington, Massachusetts, USA), 10 μL nuclease-free water,
 0.8 μL each of 10 mM fungal-specific HPLC-purified primers 5.8SR and ITS4
 (88), and 1 μL of DNA template for a total reaction volume of 25.1 μL.
 Each extract was amplified in triplicate using an Eppendorf Mastercycler
 (Hamburg, Germany) with 3 min activation at 95 °C, 30 cycles of denaturing
 at 98 °C for 20 s, annealing at 65.7 °C for 15 s and elongation at 72 °C
 for 45 s, and a final elongation at 72 °C for 5 min. PCR products were
 confirmed by electrophoresis in 1% agarose and 0.5× TBE followed by
 staining with ethidium bromide. Extracts which failed to produce PCR
 products were diluted tenfold and amplified using the above reaction
 conditions with an annealing temperature of 64.4 °C. PCR products were
 stored overnight at 4 °C and for longer periods at -20 °C. Triplicate PCR products were
 pooled and purified using the Agencourt® Ampure® XP system (Beckman
 Coulter, Indianapolis, Indiana, USA) following the manufacturer’s
 protocol, with two washes with ethanol and elution in 10 mM Tris.
 Concentration of dsDNA in each sample was measured using a Qubit 2.0
 fluorimeter (Invitrogen, Carlsbad, California, USA). Eight (2011) or ten
 (2012) samples per field were pooled at equal DNA concentration in 10 mM
 Tris, and 3-5 ng of DNA per field was shipped frozen on dry ice for
 sequencing at the University of Minnesota Genomics Center (UMGC, St. Paul,
 Minnesota, USA). PCR products from each field were
 amplified using Nextera™ indexing primers (Illumina, San Diego,
 California, USA) and 10 cycles of denaturation at 98 °C for 20 s,
 annealing at 55 °C for 15 s, and elongation at 72 °C for 1 min. Indexed
 PCR products were denatured with 8 pM NaOH in Illumina HTI buffer (20%
 PhiX) at 96 °C for 2 min prior to loading and sequencing on an Illumina
 Miseq® using Reagent Kit v3 with separate index reads. Preliminary quality
 control (QC) and demultiplexing were conducted by the UMGC.
 <b>Sequence
 </b><b>data processing.</b><i> </i>Sequences
 were processed with the PIPITS 1.4.0 pipeline (Gweon et al, 2015), which
 employs a number of different software packages, using the standard
 settings. Briefly, forward and reverse reads were merged using PEAR 0.9.8
 (http://www.exelixis-lab.org/pear), followed by quality filtering using
 FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/), and extraction
 of the fungal-specific ITS2 region using ITSx 1.0.11 (90). Dereplication,
 removal of singleton sequences or those &lt; 100 bp, clustering to 97%
 sequence identity, and chimera detection, using the UNITE Uchime 7.1
 dataset (91) as reference, were conducted with VSEARCH 2.3.0.
 Representative sequences were taxonomically assigned using the
 Warcup_retrained V2 ITS training set with RDP Classifier 2.11 to a
 taxonomic confidence level of 50% to retain a greater level of taxonomic
 resolution in the downstream analyses. An abundance table was generated
 that clustered sequences in OTUs at 97% identity. Samples were rarefied to
 132,460 sequences (the number of fungal sequences observed in the smallest
 sample) in QIIME 1.9.1 to remove the effect of differences in sequencing
 depth among samples on fungal OTU diversity. The rarefied OTU table was
 used in statistical analysis and to assign OTUs to guilds using
 FUNGuild.
description: nd = no data sample dates are in American
 format (Month/Day/Year) Slope # is qualitative; 0 =
 flat 5 = extremely steep