title: Optimal nitrogen allocation controls tree responses to elevated CO2.
abstract: Despite the abundance of experimental data, understanding of forest responses to elevated CO2 is limited. Here I show that a key to previously unexplained production and leaf area responses lies in the interplay between whole-plant nitrogen (N) allocation and leaf photosynthesis. A simple tree growth model, controlled by net growth maximization through optimization of leaf area index (LAI) and plant N, is used to analyse CO2 responses in both young, expanding and closed, steady-state canopies. The responses are sensitive to only two independent parameters, the photosynthetic capacity per leaf N (a) and the fine-root N:leaf N ratio. The model explains observed CO2 responses of photosynthesis, production and LAI in four forest free air CO2 enrichment (FACE) experiments. Insensitivity of LAI except at low LAI, increase in light-use efficiency, and photosynthetic down-regulation (as a result of reduced leaf N per area) at elevated CO2 are all explained through the combined effects on a and leaf quantum efficiency. The model bridges the gap between the understanding of leaf-level and plant-level responses and provides a transparent framework for interpreting and linking structural (LAI) and functional (net primary production (NPP):gross primary production (GPP) ratio, light-use efficiency, photosynthetic down-regulation) responses to elevated CO2.
authors.affiliation: IIASA, Institute for Applied Systems Analysis, 2361 Laxenburg, Austria.|School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Oskar
authors.initials: O
authors.lastname: Franklin
mesh_terms: D005075:Biological Evolution; D002245:Carbon Dioxide / Q000378:metabolism*; D007700:Kinetics; D009584:Nitrogen / Q000378:metabolism*; D018515:Plant Leaves / Q000502:physiology*; D012119:Respiration; D013472:Sunlight; D014197:Trees / Q000502:physiology*
chemical_list: D002245:Carbon Dioxide; D009584:Nitrogen