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Data from: Contrasting patterns of functional diversity in coffee root fungal communities associated with organic and conventionally-managed fields

Citation

Aldrich-Wolfe, Laura et al. (2020), Data from: Contrasting patterns of functional diversity in coffee root fungal communities associated with organic and conventionally-managed fields, Dryad, Dataset, https://doi.org/10.5061/dryad.q2bvq83g1

Abstract

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.

Methods

Site description and study design. 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-1 vs. 400 cm yr-1 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.

Field sampling. 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 > 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 (w/v) 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.

Molecular detection of root fungi. 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.

Sequence data processing. 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 < 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.

Usage Notes

nd = no data

sample dates are in American format (Month/Day/Year)

Slope # is qualitative; 0 = flat 5 = extremely steep

Funding

National Science Foundation, Award: DUE-0969568

National Science Foundation, Award: OIA-1355466