The lack of correlation between photosynthesis and plant growth under sink-limited conditions is a long-standing puzzle in plant ecophysiology that currently severely compromises our models of vegetation responses to global change. To address this puzzle, we applied data assimilation to an experiment in which the sink strength of Eucalyptus tereticornis seedlings was manipulated by restricting root volume. Our goals were to infer which processes were affected by sink limitation and to attribute the overall reduction in growth observed in the experiment to the effects on various carbon (C) component processes. Our analysis was able to infer that, in addition to a reduction in photosynthetic rates, sink limitation reduced the rate of utilization of nonstructural carbohydrate (NSC), enhanced respiratory losses, modified C allocation and increased foliage turnover. Each of these effects was found to have a significant impact on final plant biomass accumulation. We also found that inclusion of an NSC storage pool was necessary to capture seedling growth over time, particularly for sink-limited seedlings. Our approach of applying data assimilation to infer C balance processes in a manipulative experiment enabled us to extract new information on the timing, magnitude and direction of the internal C fluxes from an existing dataset. We suggest that this approach could, if used more widely, be an invaluable tool to develop appropriate representations of sink-limited growth in terrestrial biosphere models.
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Acknowledgements. This research was supported by the Australian Research Council (Discovery, DP DP160103436), the Hawkesbury Institute for the Environment and Western Sydney University. Martin De Kauwe acknowledges support from the ARC Centre of Excellence for Climate Extremes (CE170100023). The authors wish to thank Burhan Amiji for his technical assistance and all individuals from the Hawkesbury Institute for the Environment who helped during the experimental harvest. We thank Mathew Williams for advice on implementing the data assimilation framework.
© 2018 Author(s).