Functional traits are central to how organisms perform under different environmental conditions, interact with other species, and influence the ecosystems to which they belong. For individual trees, leaf traits influence core demographic parameters including survival and reproduction. At the species level, trait and nutrient allocation strategies can influence species distributions, co-occurrence, contribution to biogeochemical cycles and responses to land use and climate. Large scale studies of traits are challenging because trait data is expensive and time consuming to collect. Traits have generally been modelled either as species weighted averages (ignoring intraspecific variation) or directly as a function of the environment (ignoring species-specific constraints). However, both phylogenetic constraints and environmental conditions are important and their relative influence on traits is unclear. Moreover, traits distribution have generally been studied for a subsample of macronutrients individually, making it harder to effectively assess how traits covary within individuals.
To address these limitations, we used data for 8 leaf traits collected from the National Ecological Observatory Network (NEON) across Eastern United States, to build a longitudinal multilevel joint trait model that integrated phylogenetic, climate, and topographic information. The model was used to identify covariance among traits, the relative contribution of intra- and inter- specific variation, and make estimates for each trait with associated uncertainty for ~1.1 million trees from the Forest Inventory Analysis (FIA) dataset.
Results/Conclusions
On average, our model suggests that 23% of variation in traits can be explained by phylogeny alone, 18% by the environment alone, and 26% of the variation is shared by the two factors (total average variance explained being 68%). These percentages varied widely by trait with some (e.g. pigments) being determined predominantly by the environment and some (e.g. %N) by phylogeny. Our method described well the observed range of intra-species variation and patterns also for traits largely constrained by phylogeny. The marginal effect of both intra and inter-species variability varied across Eastern US for all traits, with the two drivers affecting differently across ecoregions. In conclusion, despite species average in leaf traits can be a fair estimator of their distribution, the effect of environment on intra-species variation is often as important at scale. Leveraging field inventories and open phylogenetic data it is now possible to explore the effect of rapid environmental changes on leaf traits and ecosystem biogeochemistry.