Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient

Vanessa Haverd, B Smith, M Raupach, Peter Briggs, Lars Nieradzik, Jason Beringer, Lindsay Hutley, C Trudinger, James Cleverly

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    Abstract

    The relative complexity of the mechanisms underlying savanna ecosystem dynamics, in comparison to other biomes such as temperate and tropical forests, challenges the representation of such dynamics in ecosystem and Earth system models. A realistic representation of processes governing carbon allocation and phenology for the two defining elements of savanna vegetation (namely trees and grasses) may be a key to understanding variations in tree-grass partitioning in time and space across the savanna biome worldwide. Here we present a new approach for modelling coupled phenology and carbon allocation, applied to competing tree and grass plant functional types. The approach accounts for a temporal shift between assimilation and growth, mediated by a labile carbohydrate store. This is combined with a method to maximize long-term net primary production (NPP) by optimally partitioning plant growth between fine roots and (leaves + stem). The computational efficiency of the analytic method used here allows it to be uniquely and readily applied at regional scale, as required, for example, within the framework of a global biogeochemical model. We demonstrate the approach by encoding it in a new simple carbon-water cycle model that we call HAVANA (Hydrology and Vegetation-dynamics Algorithm for Northern Australia), coupled to the existing POP (Population Orders Physiology) model for tree demography and disturbance-mediated heterogeneity. HAVANA-POP is calibrated using monthly remotely sensed fraction of absorbed photosynthetically active radiation (fPAR) and eddy-covariance-based estimates of carbon and water fluxes at five tower sites along the North Australian Tropical Transect (NATT), which is characterized by large gradients in rainfall and wildfire disturbance. The calibrated model replicates observed gradients of fPAR, tree leaf area index, basal area, and foliage projective cover along the NATT. The model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, mediated by shifting above- versus below-ground resources, and not from imposed hypotheses about the controls on tree-grass co-existence. Results support the hypothesis that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.
    Original languageEnglish
    Pages (from-to)761-779
    Number of pages19
    JournalBiogeosciences Discussions
    Volume13
    Issue number3
    DOIs
    Publication statusPublished - 11 Feb 2016

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    biomass allocation
    savanna
    phenology
    savannas
    partitioning
    grass
    grasses
    rain
    rainfall
    carbon
    vegetation dynamics
    leaves
    vegetation
    ecosystems
    photosynthetically active radiation
    biome
    disturbance
    hydrology
    physiology
    transect

    Cite this

    Haverd, Vanessa ; Smith, B ; Raupach, M ; Briggs, Peter ; Nieradzik, Lars ; Beringer, Jason ; Hutley, Lindsay ; Trudinger, C ; Cleverly, James. / Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient. In: Biogeosciences Discussions. 2016 ; Vol. 13, No. 3. pp. 761-779.
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    Haverd, V, Smith, B, Raupach, M, Briggs, P, Nieradzik, L, Beringer, J, Hutley, L, Trudinger, C & Cleverly, J 2016, 'Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient', Biogeosciences Discussions, vol. 13, no. 3, pp. 761-779. https://doi.org/10.5194/bg-13-761-2016

    Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient. / Haverd, Vanessa; Smith, B; Raupach, M; Briggs, Peter; Nieradzik, Lars; Beringer, Jason; Hutley, Lindsay; Trudinger, C; Cleverly, James.

    In: Biogeosciences Discussions, Vol. 13, No. 3, 11.02.2016, p. 761-779.

    Research output: Contribution to journalArticleResearchpeer-review

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