Global leaf-trait mapping based on optimality theory

Abstract

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 (Vcmax) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area (Narea) and mass (Nmass) are inferred using their (previously derived) empirical relationships to SLA and Vcmax. 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 Nmass and 28% for Narea. Declining species-level Nmass, 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.