,Data include microbial count data (CFUs), 16S-rRNA copy number data (qPCR), and microbial community (microbiome) data from the guts of the invasive tephritid fruit flies, melon fly (Zeugodacus cucurbitae) and medfly (Ceratitis capitata).,Resources in this dataset:,

,This Ag Data Commons submission includes the 94 sunflower paired-end sequencing FASTQ files, the corresponding 16S bacterial FASTQ files, and other relevant data to the study described below:,Host-microbe interactions are increasingly recognized as important drivers of organismal health, growth, longevity, and community-scale ecological processes. However, less is known about how genetic variation affects hosts' associated microbiomes and downstream phenotypes. We demonstrate that sunflower Helianthus annuus harbors substantial, heritable variation in microbial communities under field conditions. We show that microbial communities co-vary with heritable variation in resistance to root infection caused by the necrotrophic pathogen Sclerotinia sclerotiorum, and that plants grown in autoclaved soil showed almost complete elimination of pathogen resistance. Association mapping suggests at least 59 genetic locations with effects on both microbial relative abundance and Sclerotinia resistance. Although the genetic architecture appears quantitative, we have elucidated previously unexplained genetic variation for resistance to this pathogen. We identify new targets for plant breeding and demonstrate the potential for heritable microbial associations to play important roles in defense in natural and human-altered environments.,See README for details of each table in the spreadsheet and related information.,

,Meta-data from over 100 studies that examined induction of plant phenolic compounds following colonization by insects or microbial organisms (both pathogens and beneficials). This meta-data was used in a meta-analysis to observe that both insects and microbes consistently induce phenolic production. The meta-data covers publications that range from 2008 to 2017.,,

We compared pollinator diversity derived from environmental DNA (eDNA) extracted from flowers and DNA extracted from pulverized bulk samples of insects collected from vane traps deployed at the same sites. We used three metabarcoding primers, two of which target arthropods generally (COI-Jusino and 16S-Marquina) and one that targets bumblebees (Bombus spp., COI-Milam). Across methods, we detected 77 insect families from 9 orders. The COI-Jusino marker amplified the highest taxonomic diversity compared to 16S-Marquina and COI-Milam. More ASVs were recovered from vane traps (blue: 1357, yellow: 1542) than flowers (245), but only 23% of families and 13% of genera were shared among methods, indicating that flowers and blue and yellow vane traps may each sample different parts of the available arthropod community. Of 29 flowers with known bee visitations, only 10 had bee detections, and incomplete reference databases hindered assignment to species.

To determine how native and non-native lineages of Phragmites australis affect and respond to soil bacteria, fungi and oomycetes, we collected live rhizomes, seeds and soil from native and non-native lineages of Phragmites from 10 sites within Michigan and Ohio, USA. We propagated these field-collected samples to carry out a reciprocal-transplant plant-soil feedback experiment with multiple microbial inhibition treatments. Specifically, we investigated how each Phragmites lineage grew in soils pre-conditioned by each lineage and soils that had been pre-sterilized. Plant biomass was the main response variable collected to determine responses to microbial soil conditioning. We also used DNA meta-barcoding to identify the effects of each plant lineage on soil microbes and link plant responses to microbial communities. Specifically, DNA was extracted from soils and fungal and bacterial DNA was amplified to identify the microbial constituents. Amplicons were sequenced using Illumina MiSeq. This dataset includes outputs of bioinformatic analysis of sequences including operational taxonomic unit (OTU) generation, OTU abundance, resolved taxonomy, and environmental metadata collected in our survey. Raw sequences were uploaded to the NCBI Sequence Read Archive under SRA accession number PRJNA719385.

To determine how native and non-native lineages of Phragmites australis affect and respond to soil bacteria, fungi and oomycetes, we collected live rhizomes, seeds and soil from native and non-native lineages of Phragmites from 10 sites within Michigan and Ohio, USA. We propagated these field-collected samples to carry out a reciprocal-transplant plant-soil feedback experiment with multiple microbial inhibition treatments. Specifically, we investigated how each Phragmites lineage grew in soils pre-conditioned by each lineage and soils that had been pre-sterilized. Plant biomass was the main response variable collected to determine responses to microbial soil conditioning. We also used DNA meta-barcoding to identify the effects of each plant lineage on soil microbes and link plant responses to microbial communities. Specifically, DNA was extracted from soils and fungal and bacterial DNA was amplified to identify the microbial constituents. Amplicons were sequenced using Illumina MiSeq. This dataset includes outputs of bioinformatic analysis of sequences including operational taxonomic unit (OTU) generation, OTU abundance, resolved taxonomy, and environmental metadata collected in our survey. Raw sequences were uploaded to the NCBI Sequence Read Archive under SRA accession number PRJNA719385.

This data release includes sampling location data, the information about sequenced organisms and the SRA accession number for the 16S whole-organism sequencing data for larval aquatic insects, emergent adult insects, and two riparian spiders from Torch Lake and Gratiot Lake (N=3 sites at each Lake). Torch Lake (Houghton County, Keweenaw Peninsula, Michigan, USA) is a Great Lakes Area of Concern (AOC) and a former EPA Superfund Site and owing to a high concentration of copper and other co-occurring metals from historic mining operations, while no mining or processing activity has taken place in the nearby Gratiot Lake basin (Keweenaw County, Keweenaw Peninsula, Michigan, USA).

,Description: This dataset consists of field data (arthropods, nematodes and NDVI) collected over the course of 6 field excursions in 2015 and 2016 near TyTy, GA, in a field used for growing Miscanthus x giganteus. It also includes interpolated values of soil measurements collected in 2015 and meteorological data collected on an adjacent farm. Point-in-time measurements include all meteorological, NDVI, arthropod and nematode measurements and their derivatives. Fixed values were measurements that were held constant across all sampling dates, including location, terrain and soils measurements and their derivatives.,Dawn Olson and Jason Schmidt collected and processed arthropod count data. Jason Schmidt collected and processed spider count data and computed spider diversity. Richard Davis collected and processed nematode count data. Alisa Coffin collected and processed NDVI data and positional locations. Tim Strickland collected and processed soils data and Alisa Coffin interpolated soils values using kriging to derive values at arthropod sample locations. David Bosch collected and processed meteorological data. Lynne Seymour provided statistical expertise in deriving any estimated values (phloem feeders, parasitoids, spiders, and natural enemies). Alisa Coffin derived terrain data (elevation, slope, aspect, and distances) from publicly available datasets, transformed values (SI, WI, etc), carried out the geographically weighted regression analysis and calculated C:SE values, harmonized the full dataset, and compiled it using Esri's ArcGIS Pro 2.5. Methods for most data are published in the accompanying paper and associated supplements.,Questions about dataset development and management should be directed to Alisa Coffin (alisa.coffin@usda.gov). This work was accomplished as a joint USDA and University of Georgia project funded by a cooperative agreement (#6048-13000-026-21S). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture.,At request of the author, the data resources are under embargo. The embargo will expire on Fri, Jan 01, 2021.,

,Supplementary raw data, R scripts, and results underpinning analyses of geometric morphometric and linear data derived from scale insects; includes source code for all analyses, raw data for ostiole, leg, and body shape analyses, and alpha tables for body size analysis and ostioles analyses. Abbreviations are defined in the R script file. See README for list of resources.,