These data show grass crop and model species response to toxic chemicals (Arsenic (As)) and humic acids. Experiments were performed by collaboration between the U.S. Geological Survey, Rutgers University, and Rey Juan Carlos University. A series of individual experiments investigated beneficial effects of endophytic bacteria on grass crop growth and resilience to known plant toxicity.

These data include measurements of root and shoot lengths of Rice (Oryza sativa) and Bermuda grass (Cynodon dactylon) that were inoculated with nine endophytic bacterial isolates. The tabular data represent growth promotion and fungal infection susceptibility information. They also show the infection rate of F. oxysporum on Rice (Oryza sativa), Bermuda grass (Cynodon dactylon) and Annual Bluegrass (Poa annua) when inoculated with SY1.

These data include measurements of root and shoot lengths of Rice (Oryza sativa) and Bermuda grass (Cynodon dactylon) that were inoculated with nine endophytic bacterial isolates. The tabular data represent growth promotion and fungal infection susceptibility information. They also show the infection rate of F. oxysporum on Rice (Oryza sativa), Bermuda grass (Cynodon dactylon) and Annual Bluegrass (Poa annua) when inoculated with SY1.

We conducted a case studies testing effectiveness of a soil borne bacteria, Pseudomonas fluorescens strain D7, in controlling Bromus tectorum (cheatgrass) and in affecting the density of sown desirable seedlings. Response variables (foliar cover, aboveground biomass, and density of B. tectorum; density of sown native plants) were measured for three years after treatment.

We conducted a case studies testing effectiveness of a soil borne bacteria, Pseudomonas fluorescens strain D7, in controlling Bromus tectorum (cheatgrass) and in affecting the density of sown desirable seedlings. Response variables (foliar cover, aboveground biomass, and density of B. tectorum; density of sown native plants) were measured for three years after treatment.

The exotic grass-fire cycle is degrading semiarid rangelands, such as the vast areas of shrub-steppe in North America now invaded by fire-promoting cheatgrass. Chemical- or bio-herbicides are sprayed onto soils to inhibit the invaders, but information on chemical- or bio-herbicide effects on plant communities is limited. We asked how the plant community responded to the bioherbicide Pseudomonas fluorescens strain ACK55 (Battalion Pro®) in comparison to the separate and combined effects of the most conventional pre-emergent chemical herbicide, imazapic (Plateau®), in two cheatgrass-invaded sagebrush-steppe sites. Plant community responses are compared with soil microbial community responses in the Larger Work, and soil microbial data are available in GenBank. Plant community responses are compared with soil microbial community responses in the Larger Work, and soil microbial sequence data were deposited to the NCBI Short Read Archive (BioProject PRJNA1254875).

This dataset comprises detailed agronomic measurements from a series of wheat field trials conducted in Roma, Queensland, designed to investigate the effects of sowing depth, coleoptile type, soil strength, and other factors on plant emergence, growth, and yield. The collection includes two primary Excel (.xlsx) files: a master data sheet containing raw and processed measurements from individual plots across multiple trials (MET, Pressure, Seed Size), and an analysis workbook summarizing statistical outputs and model selections. These main files are complemented by MET Deep Tiny Tag and MET Shallow Tiny Tag .csv files. The master sheet documents plot-level data for each trial, including sowing conditions (depth, date, soil strength at multiple depths), plant traits (coleoptile length and diameter), emergence counts at multiple intervals (7, 14, 21 days after sowing), and final emergence. It also includes biomass and grain yield metrics, harvest index, grain quality parameters (protein, moisture, test weight, screenings), and maturity dates. Each plot is identified by location, replicate, treatment, and variety, with coleoptile type (long or conventional) and seed size (standard or large) noted where relevant. The analysis workbook provides statistical summaries from ANOVA and regression models, highlighting significant effects and interactions among depth, variety, coleoptile type, and soil strength. It includes model selection outputs for emergence and coleoptile traits, with R² values and p-values for various combinations of predictors. Environmental conditions such as soil strength was measured at sowing and at multiple intervals post-sowing using gravimetric and pressure-based methods. Drone imagery, EM38 surveys, and weather station data were also collected to support spatial and temporal analysis. Data was processed using GenStat with fixed and random effects models, and transformations were applied where necessary to meet distributional assumptions. The dataset includes over 70 variables, with definitions embedded in column headers and trial documentation. Codes such as LCW (long coleoptile wheat) and conventional types are used to distinguish genetic traits. The dataset is structured to support multivariate analysis and is suitable for evaluating genotype by environment interactions, emergence dynamics, and yield formation under varying agronomic conditions.

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).

This dataset shows the effects of herbicides (detected in the Great Barrier Reef catchments) on the growth rates (from cell density data) and photosynthesis (effective quantum yield) on the microalgae Chaetoceros muelleri during laboratory experiments conducted from 2018-2019. The aims of this project were to develop and apply standard ecotoxicology protocols to determine the effects of Photosystem II (PSII) and alternative herbicides on the growth and photosynthetic efficiency of the microalgae Chaetoceros muelleri. Growth bioassays were performed over 3-day exposures using herbicides that have been detected in the Great Barrier Reef catchment area(O’Brien et al. 2016). Acute effects of herbicides on the photophysiology of C. muelleri, measured by chlorophyll fluorescence as the effective quantum yield (Delta F/Fm') were investigated in 48-well plates using imaging PAM fluorometry after 24 h herbicide exposure(Mercurio et al. 2018, Schreiber et al. 2002). These toxicity data will enable improved assessment of the risks posed by PSII and alternative herbicides to microalgae for both regulatory purposes and for comparison with other taxa. Methods: The ochrophyte Chaetoceros muelleri (Lemmermann)(Lemmermann 1896) (CS-176) was purchased from the Australian National Algae Supply Service, Hobart (CSIRO). Cultures were established in Guillard’s f2 marine medium(Guillard and Ryther 1962) (0.5 ml l-1 of AlgaBoost F/2, AusAqua in 0.2 µm-filtered seawater (FSW)) and cultivated in sterile 500 ml Erlenmeyer flasks as batch cultures in exponential growth phase with weekly transfers of 70 ml of C. muelleri suspension to 350 ml f2 medium. Cultures were aerated and maintained at 26 ± 1°C, 35 psu and under a 12:12 h light:dark cycle (100-110 µmol photons m–2 s-1, Osram Lumilux Cool White 36 W). Herbicide stock solutions were prepared using PESTANAL (Sigma-Aldrich) analytical grade products (HPLC greater than or equal to 98%): diuron (CAS 330-54-1), tebuthiuron (CAS 34014-18-1), propazine (CAS 139-40-2), and haloxyfop-p-methyl (CAS 72619-32-0). The selection of herbicides was based on application rates and detection in coastal waters of the GBR(Grant et al. 2017, O’Brien et al. 2016). Stock solutions were prepared in sterile 1 l glass Schott bottles using milli-Q water or FSW. Diuron was dissolved using HPLC-grade ethanol (< 0.001 % (v/v) in exposures). Haloxyfop-p-methyl was dissolved in dimethyl sulfoxide (DMSO) (less than or equal to 0.006 % (v/v) in exposure). No solvent carrier was used for the preparation of the tebuthiuron and propazine stock solutions. Cultures of C. muelleri were exposed to a range of herbicide concentrations over a period of 72 h. Inoculum was taken from cultures in exponential growth phase (4-day-old with cell density approximately 1x106 cell ml-1). Individual C. muelleri working suspensions (3x103 cells ml-1) for each herbicide treatment were prepared in 100 ml Schott bottles and dosed with a series of herbicide concentrations. In each toxicity test, a control (no herbicide) and reference (4 µg l-1 diuron) treatments were added to support the validity of the test protocols and to monitor continued performance of the assays. Replicates (n = 5) of 10 ml of each treatment were transferred into sterile 20 ml scintillation vials and incubated at 26.0 ± 0.6 °C under a 12:12 h light:dark cycle (100-110 µmol photons m–2 s–1, Osram Lumilux Cool White 36 W). Sub-samples (0.5 ml) were taken from each replicate to measure cell densities of algal populations at 0 h and 72 h using a flow cytometer (BD Accuri C6, BD Biosciences, CA, USA). Specific growth rates (SGR) were expressed as the logarithmic increase in cell density from day i (ti) to day j (tj) as per equation (1), where SGRi-j is the specific growth rate from time i to j; Xj is the cell density at day j and Xi is the cell density at day i (OECD 2011). SGR i-j = [(ln Xj - ln Xi )/(tj - ti )] (day-1) (1) SGR relative to the control treatment was used to derive chronic