Intraspecific phenotypic variability of plant functional traits in contrasting mountain grassland habitats
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Empirical studies that link plants intraspecific variation to environmental conditions are almost lacking, despite their relevance in understanding mechanisms of plant adaptation, in predicting the outcome of environmental change and in conservation. Here, we investigate intraspecific trait variation of four grassland species along with abiotic environmental variation at high spatial resolution (n = 30 samples per species trait and environmental factor per site) in two contrasting grassland habitats in Central Apennines (Italy). We test for phenotypic adaptation between habitats, intraspecific trait-environment relationships within habitats, and the extent of trait and environmental variation. We considered whole plant, clonal, leaf, and seed traits. Differences between habitats were tested using ANOVA and ANCOVA. Trait-environment relationships were assessed using multiple regression models and hierarchical variance partitioning. The extent of variation was calculated using the coefficient of variation. Significant intraspecific differences in trait attributes between the contrasting habitats indicate phenotypic adaptation to in situ environmental conditions. Within habitats, light, soil temperature, and the availability of nitrate, ammonium, magnesium and potassium were the most important factors driving intraspecific trait-environment relationships. Leaf traits and height growth show lower variability than environment being probably more regulated by plants than clonal traits which show much higher variability. We show the adaptive significance of key plant traits leading to intraspecific adaptation of strategies providing insights for conservation of extant grassland communities. We argue that protecting habitats with considerable medium- and small-scale environmental heterogeneity is important to maintain large intraspecific variability within local populations that finally can buffer against uncertainty of future climate and land use scenarios.