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. The capacity of Amazon forests to sequester carbon is threatened by climate-change-induced shifts in precipitation patterns. However, the relativeimportance of plant physiology, ecosystem structure and trait compositionresponses in determining variation in gross primary productivity (GPP)remain largely unquantified and vary among models. We evaluate the relativeimportance of key climate constraints to GPP, comparing direct plantphysiological responses to water availability and indirect structural andtrait responses (via changes to leaf area index (LAI), roots andphotosynthetic capacity). To separate these factors we combined thesoil–plant–atmosphere model with forcing and observational data from sevenintensively studied forest plots along an Amazon drought stress gradient. Wealso used machine learning to evaluate the relative importance of individualclimate factors across sites. Our model experiments showed that variation inLAI was the principal driver of differences in GPP across the gradient,accounting for 33 % of observed variation. Differences in photosyntheticcapacity ( Vcmax and Jmax ) accounted for 21 % of variance, andclimate (which included physiological responses) accounted for 16 %.Sensitivity to differences in climate was highest where a shallow rootingdepth was coupled with a high LAI. On sub-annual timescales, the relativeimportance 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 Amazonforests, improved mapping of canopy dynamics is critical, opportunities forwhich are offered by new satellite-based remote sensing missions such asGEDI, Sentinel and FLEX.

biogeosciences

The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests