DOI: 10.5061/dryad.qnk98sfg4
title: Pollinators mediate floral microbial diversity and network under agrochemical disturbance

description: How pollinators mediate microbiome assembly in the anthosphere is a major
 unresolved question of theoretical and applied importance in the face of
 anthropogenic disturbance. We addressed this question by linking
 visitation of diverse pollinator functional groups (bees, wasps, flies,
 butterflies, beetles, true bugs and other taxa) to the key properties of
 floral microbiome (microbial α- and β-diversity and microbial network)
 under agrochemical disturbance, using a field experiment of bactericide
 and fungicide treatments on cultivated strawberries that differ in flower
 abundance. Structural equation modeling was used to link agrochemical
 disturbance and flower abundance to pollinator visitation to floral
 microbiome properties. Our results revealed that (1) pollinator visitation
 influenced the α- and β-diversity and network centrality of floral
 microbiome, with different pollinator functional groups affecting
 different microbiome properties; (2) flower abundance influenced floral
 microbiome both directly by governing the source pool of microbes and
 indirectly by enhancing pollinator visitation; and (3) agrochemical
 disturbance affected floral microbiome primarily directly by fungicide,
 and less so indirectly via pollinator visitation. These findings improve
 the mechanistic understanding of floral microbiome assembly, and may be
 generalizable to many other plants that are visited by diverse insect
 pollinators in natural and managed ecosystems.
description: <strong>Floral microbiome collection and
 sequencing</strong> Two flowers were collected
 from each plant sample each week two days post agrochemical application.
 Following Wei and Ashman (2018), flowers (without pedicel) were collected
 into a sterile 15 mL centrifuge tube using ethanol-rinsed forceps and were
 transferred to a -20 ºC freezer within two hours. For microbial DNA
 extraction, we pooled flowers standardized by size (<i>N</i> =
 1–4) per plant sample per 2-wk time period and obtained 246 total
 samples.<b> </b>Epiphytic microbes were collected by
 sonicating flowers in 3 mL phosphate-buffered saline at 40 kHz for 10 min
 and vortexing for 5 min, and were pelleted by centrifuging at 13,300 rpm
 for 5 min. Microbial DNA was extracted using Quick-DNA Fecal/Soil Microbe
 Kits (Zymo Research, Irvine, CA). Two negative controls without flower
 samples were included in the process of microbe isolation and DNA
 extraction. Samples and negative controls were sent to Argonne National
 Laboratory for bacterial (16S rRNA V5–V6 region, 799f–1115r primer pair)
 (Redford, Bowers, Knight, Linhart, & Fierer, 2010) and fungal
 (ITS1f–ITS2) (Smith & Peay, 2014) library preparation. Because the
 negative controls failed in PCRs in library preparation, the 246 samples
 were sequenced on two lanes of Illumina MiSeq paired-end 250 bp.
 <strong>Microbial sequence
 processing</strong> Demultiplexed paired-end (PE)
 reads were used for detecting bacterial and fungal amplicon sequence
 variants (ASVs) using package DADA2 v1.12.1 (Callahan et al., 2016) in R
 v3.6.0 (R Core Team, 2019) and QIIME 2 v2019.4 (Bolyen et al., 2019). For
 bacterial ASV analysis in DADA2, PE reads were trimmed and filtered
 [truncLen = c(245, 245), trimLeft = c(10, 0), maxN = 0, truncQ = 2] after
 initial quality inspection. Then end-specific variants were identified
 after taking into account sequence errors, prior to joining the PE reads
 (minOverlap = 20, maxMismatch = 4) for ASV detection. Bacterial ASVs were
 further filtered against chimeras and assigned with taxonomic
 identification based on the SILVA reference database (132 release)
 implemented in DADA2. For fungal ASV analysis in DADA2, the PE reads were
 first screened to remove potential primer contaminations. Due to the
 low-quality end (~50 bp) of the fungal reads, we then truncated reads at
 200 bp during quality filtering [truncLen = c(200, 200), maxN = 0, truncQ
 = 2] to ensure the accuracy in ASV detection. By doing so, our method
 provided a conservative estimate of fungal ASVs, due to the potential loss
 of information based on ITS length variation (Callahan et al., 2016).
 After chimera removal, fungal taxonomic assignment was conducted based on
 the UNITE reference database (v8.0 dynamic release) using QIIME
 2. Bacterial and fungal ASV tables were further
 filtered separately before conversion into microbial community matrices
 using package phyloseq (McMurdie & Holmes, 2013). First, we
 removed non-focal ASVs (Archaea, chloroplasts and mitochondria). Second,
 we filtered out low-depth samples (&lt;100 reads; <i>N</i>
 = 13 and 8 samples for bacterial and fungal data set, respectively).
 Third, we normalized per-sample reads to the same number (i.e. the median
 reads, 18788 and 37516, bacterial and fungal data set, respectively)
 following Wei and Ashman (2018). Lastly, we removed low-frequency ASVs
 (&lt;0.001% of total observations). The final community matrices
 consisted of 1237 and 1165 ASVs for bacteria (<i>N</i> = 223)
 and fungi (<i>N</i> = 240), respectively.
description: This dataset contains seven data files: (1)
 <strong>Plants_Pollinators_FloralTraits_20210311.xlsx</strong> This file contains the experimental data of each plant sample, including strawberry genotype (1, Mara Des Bois; 2, Albion; 3, Portola; 4, San Andreas), agrochemical treatment (C, water control; B, bactericide; F, fungicide; BF, bactericide and fungicide), time period (the first vs. last 2 wk, Time 1 vs. Time 2; coded as wk12 vs. wk34 in the data), experimental block (1–40), visitation (i.e. number of visiting pollinators per ~36 min per plant sample during a 2-wk time period) of individual pollinator functional groups (bees, wasps, flies, butterflies, beetles, true bugs and other taxa), and floral traits (i.e. flower size, pollen production per flower, and flower abundance). (2) <strong>FloralMicrobiome_Bacteria_16S_metadata_20210311.xlsx</strong> This file contains the experimental data of the 223 samples that were included in the final analyses of floral bacterial communities. (3) <strong>FloralMicrobiome_Bacteria_16S_CommunityMatrix_20210311.csv</strong> This file contains the bacterial community matrix of 1237 amplicon sequence variants (ASVs) across the 223 plant samples. (4) <strong>FloralMicrobiome_Fungi_ITS_metadata_20210311.xlsx</strong> This file contains the experimental data of the 240 samples that were included in the final analyses of floral fungal communities. (5) <strong>FloralMicrobiome_Fungi_ITS_CommunityMatrix_20210311.csv</strong> This file contains the fungal community matrix of 1165 amplicon sequence variants (ASVs) across the 240 plant samples. (6) <strong>ASVs_Bacteria_20210311.csv</strong> This file contains the taxonomical identification of the 1237 bacterial ASVs. (7) <strong>ASVs_Fungi_20210311.csv</strong> This file contains the taxonomical identification of the 1165 fungal ASVs.