Global leaf-trait mapping based on optimality theory

Aim: Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine-learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco-evolutionary optimality theory, to yield predictions of spatio-temporal patterns in leaf traits that can be independently evaluated. 

Innovation: Global patterns of community-mean specific leaf area (SLA) and photosynthetic capacity (V_cmax) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area (N_area) and mass (N_mass) are inferred using their (previously derived) empirical relationships to SLA and V_cmax. Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf-level measurements and/or remote-sensing methods, which are an increasingly important source of information on spatio-temporal variation in plant traits. 

Main conclusions: Model predictions evaluated against site-mean trait data from > 2,000 sites in the Plant Trait database yielded R² = 73% for SLA, 38% for N_mass and 28% for N_area. Declining species-level N_mass, and increasing community-level SLA, have both been recently reported and were both correctly predicted. Leaf-trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf-trait responses to environmental change.