Tropical savannas have been increasingly targeted for carbon sequestration by afforestation, assuming large gains in soil organic carbon (SOC) with increasing tree cover. Because savanna SOC is also derived from grasses, this assumption may not reflect real changes in SOC under afforestation. However, the exact contribution of grasses to SOC and the changes in SOC with increasing tree cover remain poorly understood. Here we combine a case study from Kruger National Park, South Africa, with data synthesized from tropical savannas globally to show that grass-derived carbon constitutes more than half of total SOC to a soil depth of 1 m, even in soils directly under trees. The largest SOC concentrations were associated with the largest grass contributions (>70% of total SOC). Across the tropics, SOC concentration was not explained by tree cover. Both SOC gain and loss were observed following increasing tree cover, and on average SOC storage within a 1-m profile only increased by 6% (s.e. = 4%, n = 44). These results underscore the substantial contribution of grasses to SOC and the considerable uncertainty in SOC responses to increasing tree cover across tropical savannas.

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.