These data were acquired from two native Mojave Desert species, Ambrosia dumosa and Larrea tridentata, from 3 sites (50 m x 50 m garden plots) distributed across the differing Mojave Desert ecotones. These sites were located on the Fort Irwin National Training Center (CA), north of Joshua Tree National Park (CA), and north of Saint George (UT). Growth and survivorship data were collected monthly from 2014 to 2018. Canopy projection data was calculated from the measurements using the mathematical equation: pi x 0.5 x L1 x 0.5 x L2, L1 is the longest diameter, L2 is the perpendicular longest diameter (cm squared). Height was measured from the ground to the tallest green leaf (cm).

Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Ambrosia dumosa within its Mojave Desert range based on known occurrences.

Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Ambrosia dumosa within its Mojave Desert range based on known occurrences.

This data publication contains 2012 flowering data for the 52 populations of big sagebrush (Artemisia tridentata) grown in 3 garden locations: Majors Flat and Ephraim in Idaho, as well as Orchard, Idaho. Data include geographical details, subspecies, julian date of flowering, and population climate variable information.

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.

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.

Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Amsinckia tessellata within its Mojave Desert range based on known occurrences.

Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Amsinckia tessellata within its Mojave Desert range based on known occurrences.