Blooming wildflowers were surveyed at 69 sites located on public and private property. The study area was the upper French Broad River basin in western North Carolina, USA, in the southern Appalachian Mountains. Sites were in forested areas (n = 51) or open fields (e.g., pastures or low-intensity hay fields, n = 12) and within 150 m of trails or roads to characterize floral resources likely to be visible to people. Sites were stratified by elevation and development intensity (e.g., building density). The same sites were used to collect bird community data (Pearson and Graves 2021) for a concurrent study (Graves et al. 2019); detailed site selection methods are published (Graves et al. 2017).
To protect privacy of private landowners, a random value between -0.01 to +0.01 degree was added to the latitude and longitude coordinates of those sites. Thus, the actual sites were located within the 1.0 km2 marked by the coordinates provided in this dataset.
Flower surveys were conducted at least once every three weeks at each site, and a subset of sites was visited weekly, from April 1 to August 8, 2014. Surveys consisted of a 50 m x 2 m belt transect established at a fixed location at each site. During each visit, we tallied the number of flowering individuals by species and estimated percent cover of flowers along the transect. Total percent cover of blooming species was estimated for each 10-m section of the 50-m transect. The mean of these estimates is provided as “average cover” of flowering individuals for each site visit.
Charismatic species were determined by conducting a search of tourism websites using the terms ‘‘western North Carolina’’, ‘‘southern Appalachian Mountains’’, or ‘‘Asheville, North Carolina’’ and listing all flowering species mentioned by name or appearing in photographs on those websites. Species that appeared on >=40% of tourism websites were considered charismatic (Graves et al. 2017).
Remotely sensed and GIS data were used to derive environmental variables: local and landscape building density, land-cover diversity, tree cover, vegetation structural diversity, estimated annual productivity, and elevation. Annual productivity and elevation were extracted at the center point of each study site. The remaining variables were extracted using buffers of 100, 200, and 1000 m, depending upon the variable.
Annual vegetation productivity was extracted from a smoothed and gap-filled MODIS Normalized Difference Vegetation Index (NDVI) dataset (Spruce et al. 2016). We calculated the 10-year (2004–2014) median of annual vegetation productivity for each study site. Elevation was extracted from the National Elevational Dataset-Digital Elevation Model (NED-DEM, USGS 2017).
Building density (building units per hectare) was quantified by counting the number of buildings located within 100 and 1000 m of the center of each study site to account for local and landscape scale effects of development intensity. Vegetation structure and tree cover were calculated from LIDAR (light detection and ranging) data within 100 m of the site center. Vegetation structural diversity was calculated using the Shannon Evenness index using the proportion (pi) of LIDAR returns in each of four vegetation strata (S = 4, i.e., herb, shrub, subcanopy, and canopy layers). Tree cover was recorded as the proportion of LIDAR returns within subcanopy or canopy layers (>2.0 m above ground, Graves et al. 2017). Land-cover diversity was calculated using Simpson’s diversity index with six land-cover categories (grassland/herb, shrubland, cropland, forest, developed, and other/water) within 200 m of each study site. SIDI ranges from 0 to 1.0 and describes the probability of two points chosen at random within a given area being in different land-cover types (McGarigal et al. 2012). We used the 2014 Cropland Data Layer (CDL, USDA-NASS 2014) and calculated SIDI using Fragstats (McGarigal et al. 2012).
See Graves et al. (2017) for more details of data collection and resulting analysis. See the WildflowerCES_detailed_methods.pdf file for bibliographic information for the cited sources.
==================== Data Sources =========================
Title: DayMet
URL: https://doi.org/10.3334/ORNLDAAC/11281
Creator: Thorton et al. 2015
Contact: ORNL DAAC uso@daac.ornl.gov
Title: MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015
URL: https://doi.org/10.3334/ORNLDAAC/1299
Creator: Spruce, J.P., G.E. Gasser, and W.W. Hargrove (2016)
Contact: ORNL DAAC uso@daac.ornl.gov
Title: National Elevation Dataset, 1/3rd arc-second Digital Elevation Models
URL: https://viewer.nationalmap.gov/
Creator: U.S. Geological Survey 2016.
Title: NC Flood Mapping: LIDAR Phase 3 all-returns data
URL: http://fris.nc.gov/fris/Download.aspx?ST=NC
Creator: Floodplain Mapping Program; Raleigh, North Carolina. NCDEM 2006.
Title: Cropland Data Layer
URL: https://nassgeodata.gmu.edu/CropScape/
Creator: USDA National Agricultural Statistics Service. USDA NASS 2014.
===========================================================