The contribution of trees and grasses to productivity of an Australian tropical savanna

Abstract

Abstract. Savanna ecosystems cover 20 % of the global land surface and account for 25 % of global terrestrial carbon uptake. They support one fifth of the world's human population and are one of the most important ecosystems on our planet. Savanna productivity is a product of the interplay between trees and grass that co-dominate savanna landscapes and are maintained through interactions with climate and disturbance (fire, land use change, herbivory). In this study, we evaluate the temporally dynamic partitioning of overstory and understory carbon dioxide fluxes in Australian tropical savanna using overstory and understory eddy covariance measurements. Over a 2-year period (September 2012 to October 2014) the overall net ecosystem productivity (NEP) of the savanna was 506.2 (±22 SE) g C m −2 yr −1 . The total gross primary productivity (GPP) was 2267.1 (±80 SE) g C m −2 yr −1 , of which the understory contributed 32 %. The understory contribution was strongly seasonal, with most GPP occurring in the wet season (40 % of total ecosystem in the wet season and 18 % in the dry). This study is the first to elucidate the temporal dynamics of savanna understory and overstory carbon flux components explicitly using observational information. Understanding grass productivity is crucial for evaluating fuel loads, as is tree productivity for quantifying the tree carbon sink. This information will contribute to a significant refinement of the representation of savannas in models, as well as improved understanding of relative tree-grass productivity and competition for resources.

Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography

“…Fractional overstorey cover at Howard Springs is approximately 50 % (Kanniah et al, 2009), and while the understorey is homogenous on the landscape scale, it can vary on the phenocam scale (i.e. field of view (FOV) of several metres; Moore et al, 2016). To account for this, GCC was calculated from four phenocams installed at the site and averaged to obtain a single daily GCC estimate.…”

“…Each year, the commencement of the dry season triggers understorey C 4 grass senescence (Andrew and Mott, 1983) and canopy leaf fall (Williams et al, 1997) as the plants prepare to survive through the rainless months of May to September (Cook and Heerdegen, 2001). This phenological change results in a transition from green to brown in the understorey, as evidenced by reduced GCC and a reduction in savanna GPP (Whitley et al, 2011;Ma et al, 2013;Moore et al, 2016).…”

Describing rainfall in northern Australia using multiple climate indices

“…Moreover, disturbances, such as fire, cyclones and grazing, are also key drivers of savanna structure and productivity which are in turn driven by rainfall patterns Bond et al, 2003;Hutley et al, 2013). Savanna grass productivity is very sensitive to rainfall and the biomass produced creates fodder for cattle and fuel for frequent burning, resulting in greenhouse gas emissions (Beringer et al, 1995(Beringer et al, , 2014Moore et al, 2015Moore et al, , 2016. Therefore, evaluating the relationship between interannual variation in rainfall and climate phenomena is crucial for predicting the responses of the water, energy and carbon cycles of savanna vegetation (Beringer et al, 2011a;Kanniah et al, 2013).…”

Preface: OzFlux: a network for the study of ecosystem carbon and water dynamics across Australia and New Zealand

“…Root water uptake is a key determinant of observed differences in the behaviour of C 3 woody and C 4 grass components of savanna ecosystems (Whitley et al, 2016). Contrasting patterns of root water uptake result in seasonal variations in phenology on canopy, understory and ecosystem scales, with consequential effects on photosynthetic production (Moore et al, 2016a;Whitley et al, 2017). Trees and grasses have been shown to have distinct light-use efficiencies, which affects photosynthesis and enables gross primary production to be tracked and inferred from phenocam networks on the ground or from space for some ecosystems (Moore et al, 2016b).…”

“…This special issue reflects the breadth of scientific research to which Ray made significant contributions, including (i) methodological aspects of observations and their interpretation Isaac et al, 2017;McHugh et al, 2017); (ii) upscaling ecosystem-scale measurements to regional and larger scales using remote sensing and physical modelling Laubach et al, 2016;Moore et al, 2016b;Restrepo-Coupe et al, 2016;Trudinger et al, 2016;Whitley et al, 2016); and (iii) analysis of carbon, water and energy cycles in response to land use and climate change, extreme weather, and fire (Bristow et al, 2016;Hinko-Najera et al, 2017;Hunt et al, 2016;Moore et al, 2016aMoore et al, , 2017Fest et al, 2017). Ray's approach to science was to always capture process understanding and integrate this into models that could be used to deliver predictions and solutions.…”