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Abstract The ubiquity of woody plant expansion across many rangelands globally has led to the hypothesis that the global rise in atmospheric carbon dioxide concentration ([CO2]) is a global driver facilitating C3 woody plant expansion. Increasing [CO2] also influences precipitation patterns seasonally and across the landscape, which often results in the prevalence of drought in rangelands. To test the potential for [CO2] to facilitate woody plant growth, we conducted a greenhouse study for 150 days to measure CO2 effects on juveniles from four woody species (Cornus drummondii C.A. Mey., Rhus glabra L., Gleditsia triacanthos L., Juniperus osteosperma Torr.) that are actively expanding into rangelands of North America. We assessed chronic water-stress (nested within CO2 treatments) and its interaction with elevated [CO2] (800 p.p.m.) on plant growth physiology for 84 days. We measured leaf-level gas exchange, tissue-specific starch concentrations and biomass. We found that elevated [CO2] increased photosynthetic rates, intrinsic water-use efficiencies and leaf starch concentrations in all woody species but at different rates and concentrations. Elevated [CO2] increased leaf starch levels for C. drummondii, G. triacanthos, J. osteosperma and R. glabra by 90, 39, 68 and 41%, respectively. We also observed that elevated [CO2] ameliorated the physiological effects of chronic water stress for all our juvenile woody species within this study. Elevated [CO2] diminished the impact of water stress on the juvenile plants, potentially alleviating an abiotic limitation to woody plant establishment in rangelands, thus facilitating the expansion of woody plants in the future. 

Abstract In highly disturbed environments, clonality facilitates plant survival via resprouting after disturbance, resource sharing among interconnected stems and vegetative reproduction. These traits likely contribute to the encroachment of deep-rooted clonal shrubs in tallgrass prairie. Clonal shrubs have access to deep soil water and are typically thought of as relatively insensitive to environmental variability. However, how leaf physiological traits differ among stems within individual clonal shrubs (hereafter ‘intra-clonal’) in response to extreme environmental variation (i.e. drought or fire) is unclear. Accounting for intra-clonal differences among stems in response to disturbance is needed to more accurately parameterize models that predict the effects of shrub encroachment on ecosystem processes. We assessed intra-clonal leaf-level physiology of the most dominant encroaching shrub in Kansas tallgrass prairie, Cornus drummondii, in response to precipitation and fire. We compared leaf gas exchange rates from the periphery to centre within shrub clones during a wet (2015) and extremely dry (2018) year. We also compared leaf physiology between recently burned shrubs (resprouts) with unburned shrubs in 2018. Resprouts had higher gas exchange rates and leaf nitrogen content than unburned shrubs, suggesting increased rates of carbon gain can contribute to recovery after fire. In areas recently burned, resprouts had higher gas exchange rates in the centre of the shrub than the periphery. In unburned areas, leaf physiology remained constant across the growing season within clonal shrubs (2015 and 2018). Results suggest single measurements within a shrub are likely sufficient to parameterize models to understand the effects of shrub encroachment on ecosystem carbon and water cycles, but model parameterization may require additional complexity in the context of fire. 

Abstract 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 of more than 0.25. For all vegetation traits, validation 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.