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Hyperspectral remote sensing has the potential to map numerous attributes of the Earth's surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers. Plain Language Summary: Understanding and identifying changes in plant diversity along broad environmental gradients requires scalable and reliable data. Spectroscopy has been shown to provide data across scales with the ability to measure plant reflectance at various extents (e.g., leaf, plot, and landscapes) with high spectral resolution and broad coverage of the electromagnetic spectrum (350–2,500 nm). In grasses, evolutionary lineage captures major axes in plant biodiversity and functional variation. We show that identifying grass evolutionary lineages from spectroscopy is possible based on common characteristics in their leaf‐level spectra. We classified 43 grass species from four sites in North America into their respective evolutionary lineages with very high accuracy (>90%) based on similarities in their leaf spectra. Classifying grass species into lineages using coarser spectral resolution data, similar to existing multispectral satellites, Sentinel 2 and Landsat 8, resulted in lower accuracy due to a loss of information from decreasing the spectral resolution. Grass lineages likely have similar spectra because of common leaf traits that evolved under similar ecological contexts. The importance of these distinctions found in the spectral reflectance of dominant grass lineages, should help our efforts in mapping and understanding grassland ecosystem function and patterns of biodiversity. Key Points: Globally dominant grass lineages are identifiable using hyperspectral leaf signaturesKey wavelengths for separating grass lineages were visible, red‐edge and shortwave infrared regions, rarely measured by multispectral sensorsHyperspectral sensors have the potential to improve remote sensing identification of grass functional types over multispectral sensors [ABSTRACT FROM AUTHOR]

This review synthesizes evidence that altered fire frequency drives discontinuous ecosystem transitions from mesic grasslands to shrublands or woodlands in the Central Great Plains, USA., Long-term fire manipulations reveal that grassland to shrubland transitions are triggered when fire-free intervals increase from 1-3 years to ≥ 3-8 years, and longer fire returns (˜10 years or more) result in transitions to woodlands. Grazing and soil properties alter these fire thresholds., Grassland to shrubland transitions are abrupt and exhibit nonlinear relationships between driver and state variables. Transitions to shrublands and woodlands exhibit hysteresis, where reintroducing frequent fires does not reverse transitions in management-relevant time-scales (decades)., Nonlinear transitions and hysteresis emerge because grasses generate positive feedbacks with fire that create strong demographic barriers for shrub and tree establishment. Fire-free intervals allow shrubs and trees to reach a size sufficient to survive fire, reproduce and disrupt the fire feedback loop through competition., Synthesis. Mesic grasslands, shrublands and woodlands constitute self-reinforcing states (alternative attractors) separated by critical fire frequency thresholds. Even without major shifts in climate, altered fire frequency can produce dramatic state changes, highlighting the importance of fire for predicting future ecosystem states. Local management should focus on prevention of unwanted transitions rather than post hoc restoration. [ABSTRACT FROM AUTHOR]

Woody encroachment is a widespread and acute phenomenon affecting grasslands and savannas worldwide. We performed a meta-analysis of 29 studies from 13 different grassland/savanna communities in North America to determine the consequences of woody encroachment on plant species richness. In all 13 communities, species richness declined with woody plant encroachment (average decline = 45%). Species richness declined more in communities with higher precipitation (r2 = 0.81) and where encroachment was associated with a greater change in annual net primary productivity (ANPP; r2 = 0.69). Based on the strong positive correlation between precipitation and ANPP following encroachment (r2 = 0.87), we hypothesize that these relationships occur because water-limited woody plants experience a greater physiological and demographic release as precipitation increases. The observed relationship between species richness and ANPP provides support for the theoretical expectation that a trade-off occurs between richness and productivity in herbaceous communities. We conclude that woody plant encroachment leads to significant declines in species richness in North American grassland/savanna communities. [ABSTRACT FROM AUTHOR]

In plants, ecologically important life history traits often display clinal patterns of population divergence. Such patterns can provide strong evidence for spatially varying selection across environmental gradients but also may result from nonselective processes, such as genetic drift, population bottlenecks and spatially restricted gene flow. Comparison of population differentiation in quantitative traits (measured as Q(ST) ) with neutral molecular markers (measured as F(ST) ) provides a useful tool for understanding the relative importance of adaptive and nonadaptive processes in the formation and maintenance of clinal variation. Here, we demonstrate the existence of geographic variation in key life history traits in the diploid perennial sunflower species Helianthus maximiliani across a broad latitudinal transect in North America. Strong population differentiation was found for days to flowering, growth rate and multiple size-related traits. Differentiation in these traits greatly exceeds neutral predictions, as determined both by partial Mantel tests and by comparisons of global Q(ST) values with theoretical F(ST) distributions. These findings indicate that clinal variation in these life history traits likely results from local adaptation driven by spatially heterogeneous environments., (© 2011 Blackwell Publishing Ltd.)