Abstract
Abstract. The capacity of Amazon forests to sequester carbon is threatened by climate-change-induced shifts in precipitation patterns. However, the relative importance of plant physiology, ecosystem structure and trait composition responses in determining variation in gross primary productivity (GPP) remain largely unquantified and vary among models. We evaluate the relative importance of key climate constraints to GPP, comparing direct plant physiological responses to water availability and indirect structural and trait responses (via changes to leaf area index (LAI), roots and photosynthetic capacity). To separate these factors we combined the soil–plant–atmosphere model with forcing and observational data from seven intensively studied forest plots along an Amazon drought stress gradient. We also used machine learning to evaluate the relative importance of individual climate factors across sites. Our model experiments showed that variation in LAI was the principal driver of differences in GPP across the gradient, accounting for 33 % of observed variation. Differences in photosynthetic capacity (Vcmax and Jmax) accounted for 21 % of variance, and climate (which included physiological responses) accounted for 16 %. Sensitivity to differences in climate was highest where a shallow rooting depth was coupled with a high LAI. On sub-annual timescales, the relative importance of LAI in driving GPP increased with drought stress (R2=0.72), coincident with the decreased importance of solar radiation (R2=0.90). Given the role of LAI in driving GPP across Amazon forests, improved mapping of canopy dynamics is critical, opportunities for which are offered by new satellite-based remote sensing missions such as GEDI, Sentinel and FLEX.
Highlights
As the entry point for carbon into the biosphere, gross primary productivity (GPP) is central to the global carbon cycle
We simulate the effect of forest structure and leaf trait distributions along the drought stress gradient, and we explore the covariation of observed leaf traits (leaf N content and LMA) and those derived from model calibrations, before using soil–plant–atmosphere model (SPA) to address the following questions
We show that indirect effects of climate exceed direct effects in driving spatial variation in GPP across an Amazon maximum climatological water deficit (MCWD) gradient (Q1)
Summary
As the entry point for carbon into the biosphere, gross primary productivity (GPP) is central to the global carbon cycle. Tropical rainforests alone account for one-third of total terrestrial GPP, assimilating ∼ 41 Pg of carbon each year (Beer et al, 2010). Carbon fluxes across the tropics are tightly coupled to climate, and water availability is a principal driver of spatial and temporal variation in GPP (Fisher et al, 2007; Von Randow et al, 2013; Beer et al, 2010; Malhi et al, 2015; Guan et al, 2015). Across Amazon forests, GPP decreases linearly with increasing seasonal water deficit (Malhi et al, 2015). Given the biogeochemical influence of Amazon forests at regional and global scales (Liu et al, 2017), accurately predicting GPP response to drought stress is critical
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