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Abstract Abstract. Using herbarium specimens spanning 133 years and field-collected measurements, we assessed intraspecific trait (leaf structural and stomatal) variability from grass species in the Great Plains of North America. We focused on two widespread, closely related grasses from the tribe Paniceae: Dichanthelium oligosanthes subsp. scribnerianum (C3) and Panicum virgatum (C4). Thirty-one specimens per taxon were sampled from local herbaria from the years 1887 to 2013 to assess trait responses across time to changes in atmospheric [CO2] and growing season precipitation and temperature. In 2021 and 2022, the species were measured from eight grasslands sites to explore how traits vary spatially across natural continental precipitation and temperature gradients. Δ13C increased with atmospheric [CO2] for D. oligosanthes but decreased for P. virgatum, likely linked to increases in precipitation in the study region over the past century. Notably, this is the first record of decreasing Δ13C over time for a C4 species illustrating 13C linkages to climate. As atmospheric [CO2] increased, C:N increased and δ15N decreased for both species and %N decreased for D. oligosanthes. Across a large precipitation gradient, D. oligosanthes leaf traits were more responsive to changes in precipitation than those of P. virgatum. In contrast, only two traits of P. virgatum responded to increases in temperature across a gradient: specific leaf area (increase) and leaf dry matter content (decrease). The only shared significant trend between species was increased C:N with precipitation. Our work demonstrates that these closely related grass species with different photosynthetic pathways exhibited various trait responses across temporal and spatial scales, illustrating the key role of scale of inquiry for forecasting leaf trait responses to future environmental change. 

Summary 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 C₃grasses and (sub)tropical C₄grasses. 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.