Extreme drought impacts have been underestimated in grasslands and shrublands globally.

Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub.

Leaf trait variation enables plants to utilize large gradients of light availability that exist across canopies of high leaf area index (LAI), allowing for greater net carbon gain while reducing light availability for understory competitors. While these canopy dynamics are well understood in forest ecosystems, studies of canopy structure of woody shrubs in grasslands are lacking. To evaluate the investment strategy used by these shrubs, we investigated the vertical distribution of leaf traits and physiology across canopies of Cornus drummondii, the predominant woody encroaching shrub in the Kansas tallgrass prairie. We also examined the impact of disturbance by browsing and grazing on these factors. Our results reveal that leaf mass per area (LMA) and leaf nitrogen per area (Na) varied approximately threefold across canopies of C. drummondii, resulting in major differences in the physiological functioning of leaves. High LMA leaves had high photosynthetic capacity, while low LMA leaves had a novel strategy for maintaining light compensation points below ambient light levels. The vertical allocation of leaf traits in C. drummondii canopies was also modified in response to browsing, which increased light availability at deeper canopy depths. As a result, LMA and Na increased at lower canopy depths, leading to a greater photosynthetic capacity deeper in browsed canopies compared to control canopies. This response, along with increased light availability, facilitated greater photosynthesis and resource-use efficiency deeper in browsed canopies compared to control canopies. Our results illustrate how C. drummondii facilitates high LAI canopies and a compensatory growth response to browsing-both of which are key factors contributing to the success of C. drummondii and other species responsible for grassland woody encroachment.

Climate variability supersedes grazing to determine the anatomy and physiology of a dominant grassland species.

Grassland ecosystems are historically shaped by climate, fire, and grazing which are essential ecological drivers. These grassland drivers influence morphology and productivity of grasses via physiological processes, resulting in unique water and carbon-use strategies among species and populations. Leaf-level physiological responses in plants are constrained by the underlying anatomy, previously shown to reflect patterns of carbon assimilation and water-use in leaf tissues. However, the magnitude to which anatomy and physiology are impacted by grassland drivers remains unstudied. To address this knowledge gap, we sampled from three locations along a latitudinal gradient in the mesic grassland region of the central Great Plains, USA during the 2018 (drier) and 2019 (wetter) growing seasons. We measured annual biomass and forage quality at the plot level, while collecting physiological and anatomical traits at the leaf-level in cattle grazed and ungrazed locations at each site. Effects of ambient drought conditions superseded local grazing treatments and reduced carbon assimilation and total productivity in A. gerardii. Leaf-level anatomical traits, particularly those associated with water-use, varied within and across locations and between years. Specifically, xylem area increased when water was more available (2019), while xylem resistance to cavitation was observed to increase in the drier growing season (2018). Our results highlight the importance of multi-year studies in natural systems and how trait plasticity can serve as vital tool and offer insight to understanding future grassland responses from climate change as climate played a stronger role than grazing in shaping leaf physiology and anatomy.

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland.

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network's (NEON's) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of readily available data products of NEON's AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total Biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2,500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14-64%. Our results suggest that currently available AOP-derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.

Microanatomical traits track climate gradients for a dominant C4 grass species across the Great Plains, USA.

BACKGROUND AND AIMS: Andropogon gerardii is a highly productive C4 grass species with a large geographic range throughout the North American Great Plains, a biome characterized by a variable temperate climate. Plant traits are often invoked to explain growth rates and competitive abilities within broad climate gradients. For example, plant competition models typically predict that species with large geographic ranges benefit from variation in traits underlying high growth potential. Here, we examined the relationship between climate variability and leaf-level traits in A. gerardii, emphasizing how leaf-level microanatomical traits serve as a mechanism that may underlie variation in commonly measured traits, such as specific leaf area (SLA). METHODS: Andropogon gerardii leaves were collected in August 2017 from Cedar Creek Ecosystem Science Reserve (MN), Konza Prairie Biological Station (KS), Platte River Prairie (NE) and Rocky Mountain Research Station (SD). Leaves from ten individuals from each site were trimmed, stained and prepared for fluorescent confocal microscopy to analyse internal leaf anatomy. Leaf microanatomical data were compared with historical and growing season climate data extracted from PRISM spatial climate models. KEY RESULTS: Microanatomical traits displayed large variation within and across sites. According to AICc (Akaike's information criterion adjusted for small sample sizes) selection scores, the interaction of mean precipitation and temperature for the 2017 growing season was the best predictor of variability for the anatomical and morphological traits measured here. Mesophyll area and bundle sheath thickness were directly correlated with mean temperature (annual and growing season). Tissues related to water-use strategies, such as bulliform cell and xylem area, were significantly correlated with one another. CONCLUSIONS: The results indicate that (1) microanatomical trait variation exists within this broadly distributed grass species, (2) microanatomical trait variability appears likely to impact leaf-level carbon and water use strategies, and (3) microanatomical trait values vary across climate gradients, and may underlie variation in traits measured at larger ecological scales.

Transcriptional responses to water stress and recovery in a drought-tolerant fescue wild grass (Festuca ovina; Poaceae).

Water stress associated with drought-like conditions is a major factor limiting plant growth and impacts productivity of natural plant communities and agricultural crops. Molecular responses of plants to water stress have been studied most extensively in model species and crops, few of which have evolved natural drought tolerance. In the current study, we examined physiological and transcriptomic responses at multiple timepoints during increasing water stress and following initial recovery from stress in a drought-tolerant C3 species, Festuca ovina. Results demonstrated non-linear transcriptomic changes during increasing stress, but largely linear declines in physiological measurements during this same period. Transcription factors represented approximately 12.7% of all differentially expressed genes. In total, 117 F. ovina homologs of previously identified and molecularly characterized drought-responsive plant genes were identified. This information will be valuable for further investigations of the molecular mechanisms involved in drought tolerance in C3 plants.

Comparing control options for time-series RNA sequencing experiments in nonmodel organisms: An example from grasses.

RNA sequencing (RNA-seq) is a widely used approach to investigate gene expression and increasingly is used in time-course studies to characterize transcriptomic changes over time. Two primary options are available as controls in time-course experiments: samples collected at the first sampling time are used as controls (temporal control, TC) and samples collected in parallel at each individual sampling time are used as controls (biological control, BC). While both approaches are used in experimental studies, we know of no analyses performed to date that directly compare effects of control type choices on identifying differentially expressed genes (DEGs) and subsequent functional analysis. In the current study, we compare experimental results using these different control types for time-course RNA-seq drought stress experiments in two wild grass species in the genus Paspalum. Our results showed BC assemblies gave a higher number of loci in both species. The number of DEGs increased with increasing stress and then decreased dramatically at the recovery time point using both control types. Expression levels of the same DEGs were highly correlated between control types in both species, ranging from r = .653 to r = .852. We also observed similar rank orders of shared enriched Gene Ontology term lists using the two different control types. Collectively, our findings suggest similar results in differential gene expression and functional annotation between control types. The ultimate choice of control type will rely on the experimental length and organism type, with labour time and sequencing costs as additional factors to be considered.

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models.

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3 grasses and (sub)tropical C4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.