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 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.

Despite a pattern of continuous morphological character variation, the long period of geographic and presumably genetic isolation warrants the delimitation of three species. Predictive modeling supports the adaptive value of acuminate apices or "drip-tips" in mesic habitats. This suggests that Cercis leaves change more rapidly than inferred from parsimony reconstruction, which has implications for the evolution of the dry floras of North America and Eurasia.

The COVID-19 pandemic has created unprecedented challenges in the way the USDA Forest Service conducts business. Standard data collection methods were immediately challenged due to travel restrictions and due to uncertainty regarding when it would be safe to return to a “business as usual” approach. These challenges were met with an inspiring collaboration between forest health specialists directly involved in the annual Aerial Detection Survey (ADS) program and remote sensing specialists from the Forest Service and academia. This group worked together to generate informative training materials, new workflows, and weekly help sessions to directly address problems that arose during this capacity building exercise. Small ad hoc teams were created to identify regionally specific program resources to enhance remote sensing utilization while supplementing information gaps where aerial detection surveys were either limited or not possible. The lessons learned from this challenge provide an opportunity to continue the exploration of combining ADS, remote sensing, and field data to deliver comprehensive information for managing the nation’s forests, while applying what is working and learning and growing from both successes and limitations. Study Implications: The 2020 USDA Forest Service’s (USFS) Aerial Detection Survey (ADS) program faced unprecedented challenges resulting from the COVID-19 pandemic, which limited surveys across nearly all USFS regions. However, this pandemic created an unexpected positive outcome through an ongoing and wide-reaching collaboration between federal, state, academic, and private sectors that has allowed for a strong and lasting collaboration across USFS regions. Moreover, this collaboration has provided a unique opportunity to optimize a combination of ADS, remote sensing, and field visits to deliver a comprehensive, robust, and near-real-time assessment of the health of our nation’s forests.

SummaryProcess-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 harbor large functional diversity, yet most Earth System Models (ESMs) summarize 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 ESMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve ESM 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.

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