To determine the potential for controlling non-native Phragmites australis through use of a cut-to-drown management strategy we performed a controlled greenhouse mesocosm experiment in Ann Arbor, MI during summer 2021. Field collected Phragmites rhizomes from one site within Michigan were propagated, cuttings taken of individual stems and potted in nursery pots. Established plants were then subjected to a complete factorial combination of three submergence treatments (no submergence, partial submergence of aboveground tissues, and compete submergence of aboveground tissues) and 3 cutting treatments (no cutting, spring cutting of above ground tissues, and summer cutting of aboveground tissues). By performing weekly monitoring of plant growth, harvesting of a subset of plant tissues and overwintering of the remaining plants, we were able to examine difference in biomass production, non-structural carbohydrate content and future viability of Phragmites plants receiving different cut-to-drown treatments.

To determine the potential for controlling non-native Phragmites australis through use of a cut-to-drown management strategy we performed a controlled greenhouse mesocosm experiment in Ann Arbor, MI during summer 2021. Field collected Phragmites rhizomes from one site within Michigan were propagated, cuttings taken of individual stems and potted in nursery pots. Established plants were then subjected to a complete factorial combination of three submergence treatments (no submergence, partial submergence of aboveground tissues, and compete submergence of aboveground tissues) and 3 cutting treatments (no cutting, spring cutting of above ground tissues, and summer cutting of aboveground tissues). By performing weekly monitoring of plant growth, harvesting of a subset of plant tissues and overwintering of the remaining plants, we were able to examine difference in biomass production, non-structural carbohydrate content and future viability of Phragmites plants receiving different cut-to-drown treatments.

These data represent the first reference genome for the invasive Phragmites australis ssp. australis (1.14 giga base pairs (Gbp)), as well as output from comparative genomic and transcriptomic analyses for invasive and native genotypes coexisting in the Great Lakes region of North America. Genome sequencing data used tillers and associated rhizome tissues collected from a single P. australis patch at the Ottawa National Wildlife Refuge near Toledo, Ohio, USA. Transcriptome analyses were produced from samples collected from three invasive and three native genotype P. australis patches from four sites around the Great Lakes in Michigan and Ohio, USA.

These data describe layered double hydroxide (LDH) mediated delivery of novel gene silencing agents (GSAs) in leaf tissue of the invasive common reed Phragmites australis ssp. australis. Double-stranded and hairpin RNAs were designed and synthesized to target paralogous genes coding for phytoene desaturase (PDS) and 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS). A leaf cutting bioassay was developed to rapidly screen GSAs for effective gene silencing action. Reduced expression of PDS and EPSPS were then characterized using reverse transcription quantitative PCR (RT-qPCR), the results of which are presented herein. Related sequences describing target genes from experimental plants are available via the National Center for Biotechnology Information's GenBank database (accession IDs: PQ634570, PQ634571, PQ634572, PQ634573).

During 2018, uncrewed aerial vehicles (UAVs or 'drones') were used to collect spatially referenced aerial imagery from 20 management units (sites) enrolled in the Phragmites Adaptive Management Framework, a collective learning program developed by the Great Lakes Phragmites Collaborative. Management units were located in Michigan, Ohio, and Wisconsin (USA). Invasive Phragmites australis (hereafter "Phragmites") had been managed at each management units some time previously by the landowner or land manager, and aerial imagery was then collected to create cover classifications distinguishing live and dead Phragmites from the surrounding landscape using object-based image analysis with training based on ground-truth field data and photos. Standard color (RGB) imagery was collected at all 20 management units, and near-infrared (NIR) imagery was collected at 2 of the 20 management units. Accuracy for the classifications was assessed by comparing cover classifications to ground truth data via confusion matrices. The accuracy associated with generating cover classifications by RGB and NIR imagery were also compared.

During 2018, uncrewed aerial vehicles (UAVs or 'drones') were used to collect spatially referenced aerial imagery from 20 management units (sites) enrolled in the Phragmites Adaptive Management Framework, a collective learning program developed by the Great Lakes Phragmites Collaborative. Management units were located in Michigan, Ohio, and Wisconsin (USA). Invasive Phragmites australis (hereafter "Phragmites") had been managed at each management units some time previously by the landowner or land manager, and aerial imagery was then collected to create cover classifications distinguishing live and dead Phragmites from the surrounding landscape using object-based image analysis with training based on ground-truth field data and photos. Standard color (RGB) imagery was collected at all 20 management units, and near-infrared (NIR) imagery was collected at 2 of the 20 management units. Accuracy for the classifications was assessed by comparing cover classifications to ground truth data via confusion matrices. The accuracy associated with generating cover classifications by RGB and NIR imagery were also compared.

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.

These data tables contain data collections from field experiments of Phragmites australis (ssp. australis) treated with known fungal endophytes. Tiller counts, tiller diameter, and tiller height measurements were taken every two weeks over an eight-week study period. Clones of Phragmites plants were collected from three different locations: Sandusky, Michigan; Bloomington, Indiana; and the Ottawa National Wildlife Refuge near Oak Harbor, Ohio. Additionally, data collections from a similar experiment of Phragmites australis (ssp. australis) treated with known fungal endophytes performed in a growth chamber are included. Tiller numbers and tiller heights were measured every three weeks over 15 total weeks for the growth chamber experiment. Plants from both experiments were collected and processed to determine dry weights and fungal communities were sequenced. All sequence data were submitted to GenBank (NCBI Accession Numbers: OM26200-OM262384).

We developed habitat suitability models for three invasive plant species: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). We applied the modeling workflow developed in Young et al. 2020, developing similar models for occurrence data, but also models trained using species locations with percent cover ≥10%, ≥25%, and ≥50%. We chose predictors from a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We selected background samples using the target background approach, and took an alternative approach to construct model ensembles by combining first percentile and ten percentile threshold rules (suitability values associated with the lowest one percent and lowest ten percent of the training data) to categorize the continuous output from each algorithm into low (below the one percentile), moderate (between the one and ten percentile), and high (above the ten percentile) suitability. Finally, we summed these to create an ensemble. This data bundle contains the merged data sets used to create the models, the composite raster files for each abundance threshold associated with each species, tabular summaries by management unit (including each species/ composite type combination), and the occurrence points with their associated cover. The spatial data are organized in a separate folder for each species, each containing 5 rasters describing potential habitat suitability for the species at the different abundance thresholds. Each of the rasters represent the composite map (composite_abundX.tif) for each abundance threshold. The bundle documentation files are: 1) 'thresholded_abundance_project_metdata.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for all three species for each thresholded abundance. 3) XX.tif where XX is the raster type explained above (abundance threshold). 4) managementSummary.csv is the tabular summaries by management unit.