Data from: Interspecific variation in prairie plant performance depends on phosphorus mining traits and mycorrhizal responsiveness in the American Northern Plains

Dataset that accompanies a research paper entitled, "Interspecific variation in prairie plant performance depends on phosphorus mining traits and mycorrhizal responsiveness in the American Northern Plains" to be submitted to a peer-reviewed journal. Journal information will be updated when paper is published. Files include a readme file, datasets, and the respective R script for analyzing the individual data files.Study includes data collected at different times and places (Montana or Colorado and Wyoming). Data are relevant to arid and semiarid mixed-grass prairie ecosystems with calcareous and alkaline subsoils, especially sites with soils of Eapa loam soil series, frigid Aridic Argiustolls or Mollisols. Except for root structural data from Colorado and Wyoming, the majority of data (plant performance, shoot manganese concentration, and root structural data) are for the focal system of northern mixed-grass prairie vegetation near Miles City, Montana which is in eastern Montana, USA (46.304583, -105.978050, elevation 849 m).The study consisted of data for a arbuscular mycorrhizal inoculant pot experiment (Reinhart et al. 2017) and trait data of plants either from that experiment or growing in the Northern Great Plains. The project was designed to improve understanding the relationship between plant performance, structural traits, and functional traits. The mycorrhizal responsiveness experiment utilized 69 plant species. Structural trait data were for a 22-species Montana dataset and an 18 species Colorado-Wyoming dataset. Data include shoot manganese concentration, mycorrhizal responsiveness (i.e. Cohen's D), total plant biomass, shoot biomass, and specific root length.Plant responsiveness to mycorrhizal fungi data were originally reported by Reinhart et al. (2017). Root structural data for plants from Colorado and Wyoming were originally reported by Mueller et al. (2024) and made publicly available as a DRYAD dataset (Blumenthal et al., 2023; doi: 10.5061/dryad.05qfttf8d).<br>