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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #375010

Research Project: Improving the Sustainability of Irrigated Farming Systems in Semi-Arid Regions

Location: Water Management and Systems Research

Title: Leaf nitrogen from the perspective of optimal plant function

Author
item DONG, NING - Macquarie University
item PRENTICE, IAIN - Imperial College
item WRIGHT, IAN - Macquarie University
item WANG, HAN - Macquarie University
item ATKIN, OWEN - Australian National University
item BLOOMFIELD, KEITH - Australian National University
item DOMINGUES, TOMAS - Universidad De Sao Paulo
item Gleason, Sean
item MAIRE, VINCENT - University Of Quebec
item ONODA, YUSUKE - Kyoto University
item POORTER, HENDRIK - Macquarie University
item SMITH, NICHOLAS - Texas Tech University

Submitted to: Journal of Ecology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2022
Publication Date: 7/18/2022
Citation: Dong, N., Prentice, I.C., Wright, I.J., Wang, H., Atkin, O.K., Bloomfield, K.J., Domingues, T., Gleason, S.M., Maire, V., Onoda, Y., Poorter, H., Smith, N.G. 2022. Leaf nitrogen from the perspective of optimal plant function. Journal of Ecology. 110(11):2585-2602. https://doi.org/10.1111/1365-2745.13967.
DOI: https://doi.org/10.1111/1365-2745.13967

Interpretive Summary: Nitrogen (N) in plant leaves is allocated to different functions. For example, N is needed in photochemistry, cell wall construction and maintenance, as well as other cellular processes. Knowing how much N is fractioned among these cellular processes is critical to predicting the functioning of N in cultivated and wild species. Here, we present and test a novel theory that leaf N contents are strongly controlled by the plant, in response to climate (temperature, evaporative demand, light), such that N is allocated to the cellular process that will achieve the greatest productivity. We show that leaf N contents (per unit area) are most closely aligned with cell wall components, and that after controlling for cell wall N, photosynthetic enzyme N explains a small but significant proportion of variation in leaf N. Moreover, the relative allocation of N (e.g., to cell wall vs enzyme) can be accurately predicted from theory considering only climate. This strongly suggests that under arid/drought conditions, where the "cost" of water is high, plants increase the amount of N that is allocated to photosynthetic enzymes, at the expense of investing in cell wall and water transport features. Conversely, when water is not limiting, plants allocate N and C to building more robust leaves and more hydraulically efficient water transport systems, rather than on enzyme functioning.

Technical Abstract: • Leaf dry mass per unit area (LMA), carboxylation capacity (Vcmax) and leaf nitrogen per unit area (Narea) embody different aspects of leaf carbon-capture strategies, and are key traits for terrestrial carbon-cycle modelling. • A global compilation of field measurements was used to infer the partial relationships of Narea to carboxylation capacity at 25°C (Vcmax25), representing photosynthetic nitrogen, and LMA, representing structural (cell-wall) nitrogen and leaf ‘economic’ (metabolic, but non-photosynthetic) nitrogen. The relationships of Vcmax to environmental variables (light, temperature, vapour pressure deficit) were also derived: theoretically, by combining the least-cost hypothesis (minimization of carboxylation plus water costs) for stomatal behaviour with the co-ordination hypothesis for Vcmax; and statistically, by multiple regression. • LMA was the most important predictor of leaf nitrogen in woody plants. Worldwide patterns in Narea were successfully predicted from LMA and Vcmax25. More than two-thirds of global Vcmax variability, in turn, was accounted for by the statistical model, whose fitted coefficients were close to theoretical expectations. • Biophysical constraints linked to LMA, and light availability and other environmental influences on photosynthetic capacity, are the first-order drivers of total leaf nitrogen. This finding suggests ing a new strategy for the prediction of leaf nitrogen in global vegetation models.