Abstract
BACKGROUND AND AIMS: Leaf functional traits are strongly tied to growth strategies and ecological processes across species, but few efforts have linked intraspecific trait variation to performance across ontogenetic and environmental gradients. Plants are believed to shift towards more resource-conservative traits in stressful environments and as they age. However, uncertainty as to how intraspecific trait variation aligns with plant age and performance in the context of environmental variation may limit our ability to use traits to infer ecological processes at larger scales. METHODS: We measured leaf physiological and morphological traits, canopy volume and flowering effort for Artemisia californica (California sagebrush), a dominant shrub species in the coastal sage scrub community, under conditions of 50, 100 and 150 % ambient precipitation for 3 years. KEY RESULTS: Plant age was a stronger driver of variation in traits and performance than water availability. Older plants demonstrated trait values consistent with a more conservative resource-use strategy, and trait values were less sensitive to drought. Several trait correlations were consistent across years and treatments; for example, plants with high photosynthetic rates tended to have high stomatal conductance, leaf nitrogen concentration and light-use efficiency. However, the trade-off between leaf construction and leaf nitrogen evident in older plants was absent for first-year plants. While few traits correlated with plant growth and flowering effort, we observed a positive correlation between leaf mass per area and performance in some groups of older plants. CONCLUSIONS: Overall, our results suggest that trait sensitivity to the environment is most visible during earlier stages of development, after which intraspecific trait variation and relationships may stabilize. While plant age plays a major role in intraspecific trait variation and sensitivity (and thus trait-based inferences), the direct influence of environment on growth and fecundity is just as critical to predicting plant performance in a changing environment.