Data to fit habitat suitability models at different invasion stages and their results to evaluate model decisions

This is a dataset containing the input and output data used in the analysis of best practices of invasive plant species distribution modeling (Young et al. 2024). We developed habitat suitability models for 13 invasive plant species at a variety of geographic ranges and different invasion stages and modeling strategies to assess the impact of predictor quality, thinning resolution, and geographic range of occurrence points on model performance. We developed a library of environmental variables at both the global scale and at the scale of the contiguous United States known to physiologically limit plant distributions (Young et al. 2024, Table S1) and relied on human input based on natural history knowledge to 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 2.2.2, Morisette et al., 2013). We used the Continuous Boyce Index (CBI) as a metric to assess model performance. This data bundle contains the merged data sets used to create the models, including location and associated environmental data, for each species, invasion stage, and modeling strategy, grouped by predictor set. In this data bundle, we have also included a dataframe of CBI values for each species, invasion stage, and modeling strategy, used in our analyses.

The species include Ailanthus altissima, Alliaria petiolata, Brassica tournefortii, Cenchrus ciliaris, Chondrilla juncea, Cirsium vulgare, Dioscorea bulbifera, Imperata cylindrica, Lonicera maackii, Lysimachia nummularia, Microstegium vimineum, Pueraria montana, and Ranunculus testiculatus.