Skip to main content

Person

Keana (Contractor) S Shadwell

SSC - Biological Science Technician

Email: kshadwell@contractor.usgs.gov
thumbnail
This dataset was used for modeling the spatial distribution of first records of non-native plants in the conterminous U.S. The input dataset (alldata_input_frdm.csv) contains 4,763 rows of data, representing 1,538 counties in the contiguous U.S., 25 decades, and 3,389 first records of invasive terrestrial plant species (Williams et al., 2024) found in the US-RIIS list (Simpson et al. 2022, ver. 2.0). Our dataset includes 12 predictor variables likely to be associated with first record locations, such as human presence, climate, human transportation vectors, and biological or academic institutions. These input data were used to create a first records distribution model (FRDM), an application of species distribution...
thumbnail
Note: this version has been superseded by INHABIT V4.0, October 2024, available at https://doi.org/10.5066/P14HNEJF. We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and...
thumbnail
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...
thumbnail
This is a dataset containing the first and second record of georeferenced observations of introduced and invasive vascular plant species in the contiguous United States (CONUS). Non-native plant species were identified using the United States Register of Introduced and Invasive Species (US-RIIS) list. After identifying a list of plants non-native to CONUS, we obtained presence data from aggregated occurrence databases, ensuring the occurrences we acquired were georeferenced (i.e., had coordinate information) and had an observation year recorded. We also identified and removed records that might indicate cultivation. From these data, the first and second record were removed and isolated. This data set contains the...
thumbnail
Note: this version has been superseded by INHABIT V4.0, October 2024, available at https://doi.org/10.5066/P14HNEJF. This is a dataset containing the potential distribution of Japanese brome (Bromus japonicus). We developed habitat suitability models for Japanese brome, as suggested by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions...
View more...
ScienceBase brings together the best information it can find about USGS researchers and offices to show connections to publications, projects, and data. We are still working to improve this process and information is by no means complete. If you don't see everything you know is associated with you, a colleague, or your office, please be patient while we work to connect the dots. Feel free to contact sciencebase@usgs.gov.