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Plant populations persist under recurrent fire via resprouting from surviving tissues (resprouters) or seedling recruitment (seeders). Woody species are inherently slow maturing, meaning that seeders are confined to infrequent fire regimes. However, for grasses, which mature faster, the relationships between persistence strategy and fire regime remain unknown. Globally, we analysed associations between fire regimes experienced by hundreds of grass species and their persistence strategy, within a phylogenetic context. We also tested whether persistence strategies are associated with morphological and physiological traits. Resprouters were associated with less frequent fire than seeders. Whilst modal fire frequencies were similar (fire return interval of 4-6 yr), seeders were restricted to regions with more frequent fire than resprouters, suggesting that greater competition with long-lived resprouters restricts seeder recruitment and survival when fire is rare. Resprouting was associated with lower leaf N, higher C:N ratios and the presence of belowground buds, but was unrelated to photosynthetic pathway. Differences between the life histories of grasses and woody species led to a contrasting prevalence of seeders and resprouters in relation to fire frequency. Rapid sexual maturation in grasses means that seeder distributions, relative to fire regime, are determined by competitive ability and recruitment, rather than time to reproductive maturity.

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.

Tree phenology mediates land-atmosphere mass and energy exchange and is a determinant of ecosystem structure and function. In the dry tropics, including African savannas, many trees grow new leaves during the dry season - weeks or months before the rains typically start. This syndrome of pre-rain green-up has long been recognized at small scales, but the high spatial and interspecific variability in leaf phenology has precluded regional generalizations., We used remote sensing data to show that this precocious phenology is ubiquitous across the woodlands and savannas of southern tropical Africa., In 70% of the study area, green-up preceded rain onset by > 20 d (42% > 40 d). All the main vegetation formations exhibited pre-rain green-up, by as much as 53 ± 18 d (in the wet miombo). Green-up showed low interannual variability (SD between years = 11 d), and high spatial variability (> 100 d)., These results are consistent with a high degree of local phenological adaptation, and an insolation trigger of green-up. Tree-tree competition and niche separation may explain the ubiquity of this precocious phenology. The ubiquity of pre-rain green-up described here challenges existing model representations and suggests resistance (but not necessarily resilience) to the delay in rain onset predicted under climate change. [ABSTRACT FROM AUTHOR]