Data were collected on seed production dynamics of long-lived plants, reproductive plant traits of those plant species, and weather variability for sites where those species live. Data include the coefficient of variation in seed production over time, the variation (coefficient of variation or standard deviation) in weather over years, and reproductive traits such as pollination mode and seed dispersal mechanism. Data series were distributed globally and encompass 920 data series containing 311 plant species. Data series are all 6 or more years in length.

We calculated estimates describing interannual variation in seed production for series of long-term seed or fruit production in 219 plant species. For each plant species, we compiled estimates of foliar nutrient concentrations as well as productivity and weather at the site of observation. Data was compiled in order to test whether interannual variation in seed production was greater in species with lower foliar nutrient concentrations or greater nutrient imbalances.

We calculated estimates describing interannual variation in seed production for series of long-term seed or fruit production in 219 plant species. For each plant species, we compiled estimates of foliar nutrient concentrations as well as productivity and weather at the site of observation. Data was compiled in order to test whether interannual variation in seed production was greater in species with lower foliar nutrient concentrations or greater nutrient imbalances.

Compiled 1086 datasets of plant seed production spanning 1900-2013 and from around the world were binned into 2-decade periods for which CV (coefficient of variation) of seed set was calculated. Skewness, dip test, mean, and kurtosis were calculated for the same periods.

Compiled 1086 datasets of plant seed production spanning 1900-2013 and from around the world were binned into 2-decade periods for which CV (coefficient of variation) of seed set was calculated. Skewness, dip test, mean, and kurtosis were calculated for the same periods.

This dataset provides the results of the characterization of shrubland vegetation at two study areas in southern Idaho, USA: the Reynolds Creek Experimental Watershed (RCEW) and Hollister. Data were collected in September and October 2014. In each study area, several 10-m x 10-m plots were randomly established that are representative of the local dominant vegetation types. Measurements are reported for both plot and individual shrub attributes. Plot measurements include shrub density and biometric data, percent shrub cover derived from line intercept transects, percent plant species and bare ground cover derived from photo analysis, and average LAI. Measurements for selected individual shrubs include height, width, length, number of stems, and LAI. Leaf samples were collected for determining LAI, specific leaf area (SLA), carbon and nitrogen concentrations, and isotopic nitrogen and carbon.

The Biophysical Properties of the Vegetation Data Set were collected as part of the larger FIFE Science effort to characterize the physical and biological properties of the sites within the FIFE study area over the life of the field experiment. These data were collected at 43 locations scattered throughout the FIFE study area between May 1987 and August 1989. The measurements of leaf area were based on an optical technique in which the area of the light beam obscured by the material under the beam is a measure of the surface area of that material relative to the total surface area that the beam covers. The resulting Leaf Area Indices (LAI) provide a relative measure of leaf area. These indices, when compared between plant samples provide an indirect and relative measure of plant biomass.

This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious

The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data set and derived biophysical parameter fields were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. This multiple resolution (1/4, 1/2 and 1 degree in latitude and longitude) biophysical parameter data set contains essential variables for the calculation of photosynthesis, and the energy and water exchange between the Earth's surface (in particular of vegetation) and the lower boundary layer of the atmosphere. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is related to the light absorption and the photosynthetic capacity of vegetation. It also serves as an intermediate variable to calculate vegetation cover fraction (Vcover), total Leaf Area Index (LAI_T), green leaf area index (LAI_G), roughness length (z0), zero plane displacement (d), and snow-free albedo. The biophysical parameters were derived assuming one canopy layer. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.