These data represent nuclear microsatellite data collected from four riparian plant species that occur in and around Grand Canyon National Park: Populus fremontii (POFR), Salix gooddingii (SAGO), Salix exigua (SAEX), and Prosopis glandulosa (PRGL). These data were collected for population genetic analysis to help inform native plant material development in Grand Canyon National Park. Leaf samples were collected at sites spanning 470 km of the Colorado River between Glen Canyon Dam and Lake Mead and in tributaries. Known revegetation areas were not sampled. We aimed to collect leaf tissue from at least 15 individuals at each sample site. If there were fewer than 15 individuals per species at a site, leaf tissue was collected from every individual. Leaves were immediately dried and stored in silica gel. For P. fremontii, S. gooddingii, and P. glandulosa, total genomic DNA was extracted from dried leaf tissue using a high-molecular weight protocol with modifications. For S. exigua, DNeasy Plant Minikits were used. We amplified 9, 9, 11, and 13 loci for P. fremontii, S. gooddingii, S. exigua, and P. glandulosa, respectively. All loci were amplified using polymerase chain reaction (PCR) and fragment analysis processed on an ABI 3730xl Genetic Analyzer with GeneScan LIZ500 internal size standard. Allele fragment sizes were scored using GeneMarker v2.2.0. Loci that were missing more than 5% of values, were not polymorphic, or could not be reliably scored were omitted. Nine loci were included for P. fremontii, six for S. gooddingii, eight for P. glandulosa, and nine for S. exigua. All species are diploid, so each microsatellite locus has two values. Associated with the microsatellite data are labels delineating what collection site each individual came from, which genetic group each individual was assigned to during statistical analyses, and information about those sites. Specifically, sites are noted as being on the Colorado River or on a tributary to it, and inside or outside of the canyon rims.

This dataset contains information on the physical traits and environmental tolerances of plant species occurring along the lower Colorado River through Grand Canyon. Due to the unique combination of plant species within the Grand Canyon, this flora shares species with many riparian areas in the western U.S.A. and represents obligate wetland to obligate upland plant species. Data for the matrix were compiled from published scientific papers, unpublished reports, plant fact sheets, existing trait databases, regional floras, and plant guides. Categorical, ordinal, and continuous data are included in this dataset. This dataset does not contain sensitive or classified data.

This dataset contains information on the physical traits and environmental tolerances of plant species occurring along the lower Colorado River through Grand Canyon. Due to the unique combination of plant species within the Grand Canyon, this flora shares species with many riparian areas in the western U.S.A. and represents obligate wetland to obligate upland plant species. Data for the matrix were compiled from published scientific papers, unpublished reports, plant fact sheets, existing trait databases, regional floras, and plant guides. Categorical, ordinal, and continuous data are included in this dataset. This dataset does not contain sensitive or classified data.

Trait-based approaches to vegetation analyses are becoming more prevalent in studies of riparian vegetation dynamics, including responses to flow regulation, groundwater pumping, and climate change. These analyses require species trait data compiled from the literature and floras or original field measurements. Gathering such data makes trait-based research time intensive at best and impracticable in some cases. To support trait-based analysis of vegetation along the Colorado River through Grand Canyon, a data set of 20 biological traits and ecological affinities for 179 species occurring in that study area was compiled. This diverse flora shares species with many riparian areas in the western USA and includes species that occur across a wide moisture gradient. This dataset contains information on the physical traits and environmental tolerances of plant species occurring along the lower Colorado River through Grand Canyon. Due to the unique combination of plant species within the Grand Canyon, this flora shares species with many riparian areas in the western U.S.A. and represents obligate wetland to obligate upland plant species. Data were compiled from published scientific papers, unpublished reports, plant fact sheets, existing trait databases, regional floras, and plant guides. Data for ordinal environmental tolerances were more readily available than were quantitative traits. Categorical, ordinal, and continuous data are included in this dataset. Also, this dataset includes data from McCoy-Sulentic et al. 2017, who measured or compiled data on specific leaf area (SLA), stem specific gravity (SSG), seed mass, and mature height of 110 plant species that occur along the Colorado River in Grand Canyon, Arizona. Additionally, they measured leaf δ13C, δ15N, % carbon, % nitrogen, and C/N ratio of 56 species with C3 photosynthesis. This dataset does not contain sensitive or classified data.

Trait-based approaches to vegetation analyses are becoming more prevalent in studies of riparian vegetation dynamics, including responses to flow regulation, groundwater pumping, and climate change. These analyses require species trait data compiled from the literature and floras or original field measurements. Gathering such data makes trait-based research time intensive at best and impracticable in some cases. To support trait-based analysis of vegetation along the Colorado River through Grand Canyon, a data set of 20 biological traits and ecological affinities for 179 species occurring in that study area was compiled. This diverse flora shares species with many riparian areas in the western USA and includes species that occur across a wide moisture gradient. This dataset contains information on the physical traits and environmental tolerances of plant species occurring along the lower Colorado River through Grand Canyon. Due to the unique combination of plant species within the Grand Canyon, this flora shares species with many riparian areas in the western U.S.A. and represents obligate wetland to obligate upland plant species. Data were compiled from published scientific papers, unpublished reports, plant fact sheets, existing trait databases, regional floras, and plant guides. Data for ordinal environmental tolerances were more readily available than were quantitative traits. Categorical, ordinal, and continuous data are included in this dataset. Also, this dataset includes data from McCoy-Sulentic et al. 2017, who measured or compiled data on specific leaf area (SLA), stem specific gravity (SSG), seed mass, and mature height of 110 plant species that occur along the Colorado River in Grand Canyon, Arizona. Additionally, they measured leaf δ13C, δ15N, % carbon, % nitrogen, and C/N ratio of 56 species with C3 photosynthesis. This dataset does not contain sensitive or classified data.

These data were collected as part of a methodological comparison for collecting riparian vegetation data. Two common methods for collecting vegetation data were used: line-point intercept and 1m2 ocular quadrats (visual cover estimates). At each site and transect, both methods were used to collect cover and composition data by four different observers. The same transects and quadrats were utilized for both methods and all observers. Field data collected included percent cover for total living foliar cover, each plant species encountered, litter, dead plant material that is still standing, and ground cover features (biological soil crust, rock, sand, and fine soil particles). Line-point intercept data were collected at 25 cm intervals along each transect and at four points along the edge of each 1m2 quadrat. Since transects varied in length, the number of data points collected along each transect also varied. A pin flag was dropped vertically to the ground at 25 cm intervals and every plant species and ground cover element that touched the pin flag was recorded in the order it touched the pin flag from top to bottom, including any species that would touch the pin flag if it continued upward indefinitely. Each species was only recorded once at each point. Ocular quadrat data were collected at each of the 1 m2 quadrats. Cover estimates were recorded to the nearest 5% other than those estimates under 5% which were recorded as either 1% or “trace”. Observers calibrated their ocular estimates at the beginning of sampling and when a new observer started sampling. Observers were given reference cards illustrating multiple levels of percent cover (1 – 95%), which were used during calibration and throughout data collection. Five observers with three levels of experience participated in this study. Two observers had extensive experience with identification of plant species in the study area, as well as with the methods used. One observer was familiar with the methods as well as riparian plant identification, but had not previously worked in this study area. Two observers had not worked in this system or with these methods before, but had experience conducting vegetation surveys. All observers received on-site training. At each site, four observers sampled the entire site using both field methods.

These data were collected as part of a methodological comparison for collecting riparian vegetation data. Two common methods for collecting vegetation data were used: line-point intercept and 1m2 ocular quadrats (visual cover estimates). At each site and transect, both methods were used to collect cover and composition data by four different observers. The same transects and quadrats were utilized for both methods and all observers. Field data collected included percent cover for total living foliar cover, each plant species encountered, litter, dead plant material that is still standing, and ground cover features (biological soil crust, rock, sand, and fine soil particles). Line-point intercept data were collected at 25 cm intervals along each transect and at four points along the edge of each 1m2 quadrat. Since transects varied in length, the number of data points collected along each transect also varied. A pin flag was dropped vertically to the ground at 25 cm intervals and every plant species and ground cover element that touched the pin flag was recorded in the order it touched the pin flag from top to bottom, including any species that would touch the pin flag if it continued upward indefinitely. Each species was only recorded once at each point. Ocular quadrat data were collected at each of the 1 m2 quadrats. Cover estimates were recorded to the nearest 5% other than those estimates under 5% which were recorded as either 1% or “trace”. Observers calibrated their ocular estimates at the beginning of sampling and when a new observer started sampling. Observers were given reference cards illustrating multiple levels of percent cover (1 – 95%), which were used during calibration and throughout data collection. Five observers with three levels of experience participated in this study. Two observers had extensive experience with identification of plant species in the study area, as well as with the methods used. One observer was familiar with the methods as well as riparian plant identification, but had not previously worked in this study area. Two observers had not worked in this system or with these methods before, but had experience conducting vegetation surveys. All observers received on-site training. At each site, four observers sampled the entire site using both field methods.

These data were compiled to investigate the evolutionary history of Hilaria jamesii, Hilaria mutica, and Hilaria rigida. The data release consists of two tab delimited text files that may be used to infer population structure (viva_structure.stru) or relationships among sampling localities (viva.phylip). Files record genetic variation on an individual (.stru) or sampling locality (.phylip) level. These files may be opened and edited in a text editor program, such as Notepad ++ (PC) or BBEdit (Mac). The .phylip file can be uploaded to phyML or SVDQuartets to generate a tree-based visualization of relationships ( http://www.atgc-montpellier.fr/phyml/ or https://paup.phylosolutions.com, repectively). The .stru file can be used in the STRUCTURE program (Falush et al. 2003) to estimate population structure.

These data were compiled to investigate the evolutionary history of Hilaria jamesii, Hilaria mutica, and Hilaria rigida. The data release consists of two tab delimited text files that may be used to infer population structure (viva_structure.stru) or relationships among sampling localities (viva.phylip). Files record genetic variation on an individual (.stru) or sampling locality (.phylip) level. These files may be opened and edited in a text editor program, such as Notepad ++ (PC) or BBEdit (Mac). The .phylip file can be uploaded to phyML or SVDQuartets to generate a tree-based visualization of relationships ( http://www.atgc-montpellier.fr/phyml/ or https://paup.phylosolutions.com, repectively). The .stru file can be used in the STRUCTURE program (Falush et al. 2003) to estimate population structure.

These data were compiled to investigate the demographic, phylogeographic, and adaptation history of Hilaria jamesii. The data release consists of three tab delimited text files that may be used to infer population structure or putative adaptive loci (hija_adaptation_dataset.stru), relationships among sampling localities (hija_phylogeny_dataset.phylip), or genetic diversity statistics (hija_diversity_stats.vcf). All files record genetic variation on an individual (.stru and .vcf) or sampling locality (.phylip) level. The .vcf file contains all of the information contained in the other files, but the file structures vary based on the programs used for analysis. These files may be opened and edited in a text editor program, such as Notepad ++ (PC) or BBEdit (Mac). The .vcf file can be loaded into the Stacks population program (Catchen et al. 2013) to calculate genetic diversity statistics. The .phylip file can be uploaded to phyML to generate a tree-based visualization of relationships ( http://www.atgc-montpellier.fr/phyml/). The .stru file can be used in the STRUCTURE program (Falush et al. 2007) to estimate population structure.