Informing models through empirical relationships between foliar phosphorus, nitrogen and photosynthesis across diverse woody species in tropical forests of Panama [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 333.7 Land, recreational and wilderness areas, energy
• Thông tin xuất bản: Oak Ridge, Tenn. : Oak Ridge, Tenn. : Oak Ridge National Laboratory ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2016
• Mô tả vật lý: Size: p. 1425-1437 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 260679
• Chủ đề: 1. 54 environmental sciences; 2. 60 applied life sciences; 3. Maximal rubisco carboxylation rate (vcmax); 4. Maximum electron transport rate (jmax); 5. N : p ratio; 6. Nitrogen (n); 7. Phosphorus (p); 8. Photosynthesis; 9. Traits; 10. Tropical forest

Tóm tắt

 Here, our objective was to analyze and summarize data describing photosynthetic parameters and foliar nutrient concentrations from tropical forests in Panama to inform model representation of phosphorus (P) limitation of tropical forest productivity. Gas exchange and nutrient content data were collected from 144 observations of upper canopy leaves from at least 65 species at two forest sites in Panama, differing in species composition, rainfall and soil fertility. Photosynthetic parameters were derived from analysis of assimilation rate vs internal CO<
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  concentration curves (<
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 ), and relationships with foliar nitrogen (N) and P content were developed. The relationships between area-based photosynthetic parameters and nutrients were of similar strength for N and P and robust across diverse species and site conditions. The strongest relationship expressed maximum electron transport rate (J<
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 ) as a multivariate function of both N and P, and this relationship was improved with the inclusion of independent data on wood density. Models that estimate photosynthesis from foliar N would be improved only modestly by including additional data on foliar P, but doing so may increase the capability of models to predict future conditions in P-limited tropical forests, especially when combined with data on edaphic conditions and other environmental drivers.