TY - JOUR
T1 - Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales
AU - Wu, Jin
AU - Guan, Kaiyu
AU - Hayek, Matthew
AU - Restrepo-Coupe, Natalia
AU - Wiedemann, Kenia T.
AU - Xu, Xiangtao
AU - Wehr, Richard
AU - Christoffersen, Bradley O.
AU - Miao, Guofang
AU - da Silva, Rodrigo
AU - de Araujo, Alessandro C.
AU - Oliviera, Raimundo C.
AU - Camargo, Plinio B.
AU - Monson, Russell K.
AU - Huete, Alfredo R.
AU - Saleska, Scott R.
N1 - Funding Information:
Funding for this research was provided by NSF PIRE (#0730305), the NASA Terra-Aqua Science Program (#NNX11AH24G), the University of Arizona's Agnese Nelms Haury Program in Environment and Social Justice, U.S. DOE's GoAmazon Project (# DE-SC0008383), and by a NASA Earth and Space Science Fellowship (NESSF) to J.W. B.O.C. and J.W. were supported in part by the DOE (BER) NGEE-Tropics subcontract to LANL and BNL, respectively. Thanks to Dr. John Norman for the advise on the ‘Weiss & Norman, ’ model and comments on the first draft of this work. We also thank two anonymous reviewers for their constructive comments to improve the scientific rigor and clarity of the manuscript.
Publisher Copyright:
© 2016 John Wiley & Sons Ltd
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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 (R2= 0.77) to interannual (R2= 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.
AB - 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 (R2= 0.77) to interannual (R2= 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.
KW - environmental limitation
KW - leaf demography
KW - leaf quality
KW - leaf quantity
KW - light-use efficiency
KW - phenology
KW - physiology
KW - temperature sensitivity on productivity
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U2 - 10.1111/gcb.13509
DO - 10.1111/gcb.13509
M3 - Article
C2 - 27644012
AN - SCOPUS:84991037830
SN - 1354-1013
VL - 23
SP - 1240
EP - 1257
JO - Global change biology
JF - Global change biology
IS - 3
ER -