Your browser doesn't support javascript.
loading
Whole-plant optimality predicts changes in leaf nitrogen under variable CO2 and nutrient availability.
Caldararu, Silvia; Thum, Tea; Yu, Lin; Zaehle, Sönke.
Affiliation
  • Caldararu S; Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, 07745, Germany.
  • Thum T; Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, 07745, Germany.
  • Yu L; Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, 07745, Germany.
  • Zaehle S; Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, 07745, Germany.
New Phytol ; 225(6): 2331-2346, 2020 03.
Article in En | MEDLINE | ID: mdl-31737904
ABSTRACT
Vegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes. We hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criterion based on whole-plant growth, and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO2 Enrichment and N fertilisation experimental sites. We show that a model based solely on canopy carbon export fails to reproduce observed patterns and predicts decreasing leaf N content with increased N availability. However, an optimal model which maximises total plant growth can correctly reproduce the observed patterns. The optimality model we present here is a whole-plant approach which reproduces biologically realistic changes in leaf N and can thereby improve ecosystem-level predictions under transient conditions.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ecosystem / Nitrogen Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: New Phytol Journal subject: BOTANICA Year: 2020 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ecosystem / Nitrogen Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: New Phytol Journal subject: BOTANICA Year: 2020 Document type: Article Affiliation country: