Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales |
| |
| Authors: | Jin Wu Kaiyu Guan Matthew Hayek Natalia Restrepo‐Coupe Kenia T Wiedemann Xiangtao Xu Richard Wehr Bradley O Christoffersen Guofang Miao Rodrigo da Silva Alessandro C de Araujo Raimundo C Oliviera Plinio B Camargo Russell K Monson Alfredo R Huete Scott R Saleska |
| |
| Institution: | 1. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA;2. Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA;3. National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA;4. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA;5. Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Sydney, NSW, Australia;6. Department of Geosciences, Princeton University, Princeton, NJ, USA;7. Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA;8. Department of Forestry and Environmental Resources, North Carolina State University at Raleigh, Raleigh, NC, USA;9. Department of Environmental Physics, University of Western Para‐UFOPA, Para, Brazil;10. Embrapa Amazonia Oriental, Belem, Brasil;11. Embrapa Amaz?nia Oriental, Santarém, Brasil;12. Laboratorio de Ecologia Isotopica, Centro de Energia Nuclear na Agricultura (CENA), Universidade de Sao Paulo, Piracicaba, SP, Brasil;13. Department of Ecology and Evolutionary Biology and Laboratory of Tree Ring Research, University of Arizona, Tucson, AZ, USA |
| |
| 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 (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. |
| |
| Keywords: | environmental limitation leaf demography leaf quality leaf quantity light‐use efficiency phenology physiology temperature sensitivity on productivity |
|
|
