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

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.

咖啡根际真菌群落的结构与功能受作物生境调控。咖啡的种植管理模式存在连续梯度，从全日照下常规施用农药与化肥的模式，到林下遮阴的有机管理模式，本研究以咖啡田作为恒定宿主系统，对比遮阴条件与农业投入方式显著不同的生境下的根际真菌群落差异。我们在哥斯达黎加两个区域的25块咖啡田中，对其遮阴与土壤环境进行了表征，这些田块分别采用常规、有机或过渡型管理模式。我们针对这些咖啡根中的真菌DNA的内转录间隔区2（ITS2，internal transcribed spacer 2）区域进行扩增，并通过高通量测序表征根际真菌群落。将序列归类为功能群，以解析不同咖啡田生境下真菌功能多样性与营养结构的差异。

相较于常规管理田块，有机田块的遮阴程度更高、遮阴树物种丰富度更大、枯落物更多，土壤酸性更弱，同时土壤硝态氮有效性更低，铜、钙、镁含量更高；不过有机与常规田块在遮阴、钙与镁含量上的差异受区域影响。有机与常规管理田块的根际真菌丰富度及群落组成差异，同样与遮阴程度、土壤酸度、硝态氮及铜含量相关。营养结构随咖啡田管理模式而异：腐生真菌、植物病原菌与寄生真菌的多样性在有机田块中更高，植物病原菌的丰度也显著高于常规田块，而腐生-寄生复合型真菌在常规田块中更为丰富。上述差异反映了生境差异，且受区域因素调控。

研究意义
根际真菌在生态系统中扮演关键角色，作为养分循环者、病原菌与共生伙伴，但目前对于哪些环境因子以及农业管理方式如何塑造根际真菌群落及其功能多样性，我们仍知之甚少。这项针对咖啡农业生态系统的田间研究表明，有机管理不仅能够促进真菌整体多样性的提升，还能维持腐生真菌、植物病原菌与寄生真菌的更高丰富度，这对于农业系统中的养分循环效率与植物病原菌种群调控具有重要意义。此外，遮阴管理以及杀菌剂与合成化肥的使用，同样会改变咖啡农业生态系统的营养结构。

研究方法
样地描述与研究设计
本研究选取哥斯达黎加两个具有山前湿润森林气候的咖啡种植区域：蒙特维多云雾森林保护区（Monteverde，10°19'27.8"N，84°50'30.1"W）与圣维托（San Vito，8°52'41.1"N，82°57'03.1"W）。两地土壤均为火山灰土（Andisols），属于火山土壤类型，具有高有机质含量、强淋溶特性，pH值介于5.6-5.8之间。蒙特维多云雾森林保护区的年均降雨量略低于圣维托（300 cm·yr⁻¹ 对比 400 cm·yr⁻¹）。

本研究共纳入25块咖啡田。其中蒙特维多云雾森林保护区共采样13块田：6块于2011年5月25-28日采样，7块于2012年6月1-4日采样。圣维托区域分别于2011年5月31日-6月3日、2012年6月7-11日各采样6块田。在每个样地，我们会采访农户或农场管理者，以获取该田块使用的除草剂、杀虫剂、杀菌剂与化肥类型，种植的咖啡品种、田块与植株树龄、既往土地利用方式与修剪制度。根据农户反馈，将田块划分为三类：常规管理田（施用合成化肥与农药）、有机管理田（通过有机认证或过去5年未使用合成化肥与农药）、过渡型常规田（正处于从常规向有机管理转型的过程中，或过去1-3年未使用合成化肥与农药）。

样地采样
针对每块田块，我们记录遮阴树物种丰富度、防风林类型以及咖啡植株的物候状态（营养生长、开花、结青果或成熟果）。除1块田的咖啡植株处于营养生长期外，其余所有田块在采样时均结有青果（未成熟）或青果与红果（成熟果）。在每块田块中，我们设置20 m×20 m的样方，样方距离田块边缘≥5 m，且能够代表该田块的遮阴树密度。使用Garmin eTrex Venture HC®（佳明公司，沙夫豪森，瑞士）记录大致海拔。使用带凸面镜的球面测积仪（Forestry Suppliers，杰克逊，密西西比州，美国）按照制造商说明书计算样方中心的冠层覆盖百分比。使用罗盘测量样方朝向，2011年采用定性方式记录样方坡度，2012年则使用测斜仪进行定量测量。通过测量5行咖啡植株的行间距离，以及5组植株的株间距离，估算咖啡种植密度。

在每个样方内，沿隔行每隔5 m采样1株咖啡植株，每个样方共采样20株。对每株植株，在滴水线处测量枯落物厚度，使用直径2 cm的土钻采集约20 cm深度的土壤样品。从每间隔1株的采样植株中，采集1-15 cm深度的细根样本3-5段并混合，每个样方共采集10株植株的根样。同一块田块的土壤样品混合后，置于纸袋中风干并室温储存。

实验室处理
在实验室中，每份根样先用自来水冲洗后分为两份。一份子样置于1% KOH（质量体积比）溶液中，用于分析丛枝菌根（AM，arbuscular mycorrhizal）真菌的根定殖情况（Aldrich-Wolfe等，已投稿）；另一份子样置于Drierite（W.A. Hammond Company，泽尼亚，俄亥俄州，美国）中干燥，用于DNA提取。相较于新鲜或冷冻样本，干燥根样的总DNA产量无显著降低，但可能会降低真菌DNA的提取效率(86)，同时可避免冷冻样本在运输过程中解冻导致的DNA降解风险(87)。每年采样期结束后，用于DNA提取的土壤与干燥根样被运往美国并室温储存。从每块田块中选取2-3份土壤子样，由北达科他州立大学土壤测试实验室（法戈，北达科他州，美国）分析土壤养分有效性、水提pH值以及烧失量（LOI，loss on ignition）测定的有机质含量。后续统计分析均采用每块田块的平均值。

根际真菌的分子检测
使用6颗直径2.33 mm的铬钢珠（Biospec Products，巴特尔斯维尔，俄克拉荷马州，美国），在涡旋适配器（Mo Bio Laboratories，卡尔斯巴德，加利福尼亚州，美国）中配合Vortex-Genie® 2混合器涡旋1小时，将干燥根样研磨成粉末。每份样本取20 mg，使用Qiagen DNeasy植物微量试剂盒（Qiagen，日耳曼敦，马里兰州，美国）按照制造商说明书提取DNA（两次洗脱，每次50 μL），每个田块提取8-10份根样，提取的DNA储存于-20 °C。

使用聚合酶链式反应（PCR，polymerase chain reaction）扩增每份DNA提取物的内转录间隔区2（ITS2）：反应体系包含12.5 μL 2× Kapa HiFi Hotstart Ready Mix（Kapa Biosystems，威尔明顿，马萨诸塞州，美国）、10 μL 无核酸酶水、0.8 μL 浓度为10 mM的真菌特异性HPLC纯化引物5.8SR与ITS4(88)，以及1 μL DNA模板，总反应体积为25.1 μL。每份提取物设置3次重复扩增，使用Eppendorf Mastercycler（汉堡，德国）进行扩增：95 °C活化3 min，30个循环（98 °C变性20 s，65.7 °C退火15 s，72 °C延伸45 s），最后72 °C终延伸5 min。通过1%琼脂糖凝胶与0.5× TBE缓冲液电泳，再经溴化乙锭染色验证PCR产物。对于未扩增出产物的提取物，将其稀释10倍后，采用上述反应体系，以64.4 °C作为退火温度重新扩增。PCR产物短期储存于4 °C，长期储存于-20 °C。

将3次重复的PCR产物混合，按照制造商说明书使用Agencourt® Ampure® XP系统（Beckman Coulter，印第安纳波利斯，印第安纳州，美国）进行纯化，采用乙醇洗涤两次，最后用10 mM Tris溶液洗脱。使用Qubit 2.0荧光计（Invitrogen，卡尔斯巴德，加利福尼亚州，美国）测定样品中双链DNA（dsDNA，double-stranded DNA）的浓度。将每个田块的8份（2011年）或10份（2012年）样本按照相同DNA浓度混合于10 mM Tris溶液中，每份田块取3-5 ng DNA，置于干冰上冷冻运输至明尼苏达大学基因组中心（UMGC，圣保罗，明尼苏达州，美国）进行测序。

使用Nextera™索引引物（Illumina，圣地亚哥，加利福尼亚州，美国）扩增每个田块的PCR产物，扩增程序为：98 °C变性20 s，55 °C退火15 s，72 °C延伸1 min，共10个循环。将带索引的PCR产物用8 pM NaOH与Illumina HTI缓冲液（含20% PhiX）在96 °C变性2 min，随后加载至Illumina Miseq®平台，使用Reagent Kit v3进行双索引读取测序。初步质量控制（QC，quality control）与双端拆分由UMGC完成。

序列数据处理
使用PIPITS 1.4.0流程（Gweon等，2015）处理序列，该流程整合多款软件工具，采用标准参数设置。简要流程如下：使用PEAR 0.9.8合并正向与反向reads(http://www.exelixis-lab.org/pear)，随后使用FASTX-Toolkit(http://hannonlab.cshl.edu/fastx_toolkit/)进行质量过滤，再通过ITSx 1.0.11(90)提取真菌特异性ITS2区域。使用VSEARCH 2.3.0进行去重复、移除单例序列与长度<100 bp的序列、以97%序列同一性聚类、并以UNITE Uchime 7.1数据集(91)作为参考进行嵌合体检测。使用Warcup_retrained V2 ITS训练集结合RDP Classifier 2.11对代表性序列进行分类学注释，分类置信度阈值设置为50%，以保留后续分析中的更高分类学分辨率。生成以97%同一性聚类的操作分类单元（OTU，operational taxonomic unit）丰度表。使用QIIME 1.9.1将所有样本抽平至132,460条序列（最小样本中的真菌序列数），以消除测序深度差异对真菌OTU多样性的影响。使用抽平后的OTU表进行统计分析，并通过FUNGuild将OTU分配至功能群。