Abstract
Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 = 0.65 to 0.72) but less so for the overstory (r2 = 0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 = 0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.
Highlights
Savanna ecosystems are defined by the coexistence of trees and grasses and have evolved to dominate one-fifth of the terrestrial land surface (Scholes and Archer, 1997; Grace et al, 2006)
The incorporation of phenocam green chromatic coordinate (GCC) into the light use efficiency (LUE) model improved the estimate of understory gross primary productivity (GPP) substantially (Table 2, Fig. 9c, d). This was most apparent with the combined use of GCC and evaporative fraction (EF) in the LUE model, which produced the best correlation (r = 0.86), lowest root mean square error (RMSE) (1.42 g C m−2) and lowest relative predictive error (RPE) (39.59 %; Table 2, Fig. 9). These results show that while EF is an important factor for GPP, greenness phenology is key for estimating understory productivity
White balance was not fixed for this study, we found that the GCC and excess green index (ExG) time series matched well with GPP estimates regardless, once smoothed to an 8-day running mean time series to coincide with Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data
Summary
Savanna ecosystems are defined by the coexistence of trees and grasses and have evolved to dominate one-fifth of the terrestrial land surface (Scholes and Archer, 1997; Grace et al, 2006). Fire plays a role in shaping savanna phenology and structure, with recurrences often every 1–5 years (Hoffmann et al, 2012; Beringer et al, 2015). Drought and land-use change are additional disturbances that commonly occur in savannas (Hutley and Beringer, 2011). These complex interactions are believed to be the primary reason for the co-dominance of trees and grasses in savanna ecosystems, as well as for the phenological variability displayed (Bond et al, 2003; Van Langevelde et al, 2003; Bond, 2008; Hanan and Lehmann, 2010; Lehmann et al, 2014)
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