Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales

Abstract

Gross ecosystem productivity (GEP) in tropical forests varies both with the environment and with biotic changes in photosynthetic infrastructure, but our understanding of the relative effects of these factors across timescales is limited. Here, we used a statistical model to partition the variability of seven years of eddy covariance-derived GEP in a central Amazon evergreen forest into two main causes: variation in environmental drivers (solar radiation, diffuse light fraction, and vapor pressure deficit) that interact with model parameters that govern photosynthesis and biotic variation in canopy photosynthetic light-use efficiency associated with changes in the parameters themselves. Our fitted model was able to explain most of the variability in GEP at hourly (R = 0.77) to interannual (R = 0.80) timescales. At hourly timescales, we found that 75% of observed GEP variability could be attributed to environmental variability. When aggregating GEP to the longer timescales (daily, monthly, and yearly), however, environmental variation explained progressively less GEP variability: At monthly timescales, it explained only 3%, much less than biotic variation in canopy photosynthetic light-use efficiency, which accounted for 63%. These results challenge modeling approaches that assume GEP is primarily controlled by the environment at both short and long timescales. Our approach distinguishing biotic from environmental variability can help to resolve debates about environmental limitations to tropical forest photosynthesis. For example, we found that biotically regulated canopy photosynthetic light-use efficiency (associated with leaf phenology) increased with sunlight during dry seasons (consistent with light but not water limitation of canopy development) but that realized GEP was nonetheless lower relative to its potential efficiency during dry than wet seasons (consistent with water limitation of photosynthesis in given assemblages of leaves). This work highlights the importance of accounting for differential regulation of GEP at different timescales and of identifying the underlying feedbacks and adaptive mechanisms.

Carbon exchange in an Amazon forest: from hours to years

“…Observations of RE are nighttime hours during which NEE was measured; observations of GEE are daytime hours during which the 50 h running average RE was subtracted from measured NEE. Partitioned GEE is not a direct observation but represents the lowest-parameter approximation of a direct measurement (GEE = NEE − RE; see Wu et al, 2017). Our GEE and RE results are limited by not accounting for partitioning bias.…”

“…Atmospheric moisture and diffuse radiation, in addition to radiation, are also known to affect photosynthesis at tropical sites on short timescales (Kiew et al, 2018), by affecting stomatal closure and hence controlling the degree to which photosynthetic uptake saturates at high PAR. We tested a higher-parameter model based on a light and moisture model representing exogenous changes to LUE from Wu et al (2017) to examine whether these meteorological variables added explanatory power to our model at monthly and longer timescales. This model adjusts LUE by multiplying terms that account for effects of vapor pressure deficit (VPD: 1 − k VPD ) and cloudiness index (CI: 1 − 1 − k CI ), a statistical proxy for diffuse radiation.…”

“…Upper-level soil moisture, for instance, exerts seasonal controls upon NEE at various tropical sites differently depending on terrain Kiew et al, 2018) but is not included in the model because it was insignificantly associated with GEP (Wu et al, 2017) or RE at this site after we controlled for other variables, including wet-and dry-season onset, in our model. Examples of a change in intrinsic ecosystem efficiency may occur in the aftermath of a drought -during which leaf stomates close, causing the ecosystem to sequester less CO 2 per unit incident PAR than average -or a storm inducing widespread mortality and a pulse of CWD during which RE would be higher than average for a given season or year.…”

“…A higher-parameter model with VPD and diffuse radiation from Wu et al (2017) explained additional variance in hourly NEE but not in monthly NEE (Table S1 in the Supplement). The BIC score for this model (−31.4) was greater (more negative) than that from our main model (−35.6; Eq.…”

“…Partitioning the exogenous and endogenous influences upon eddy covariance NEE is possible using statistical modeling (Barford et al, 2001;Yadav et al, 2010;Wu et al, 2017). To partition influences upon NEE in a 20-year eddy flux record in a temperate New England forest, Urbanski et al (2007) used a statistical modeling approach: by representing hourly NEE merely as response to exogenous meteorology and annually integrating their results, they concluded that meteorology did not explain the accelerated uptake seen in annually integrated NEE.…”