Journal
ECOSYSTEMS
Volume 12, Issue 6, Pages 915-926Publisher
SPRINGER
DOI: 10.1007/s10021-009-9265-1
Keywords
carbon sequestration; ecosystem respiration; grassland; gross primary productivity; net ecosystem carbon exchange; precipitation regimes; nitrogen deposition; species composition
Categories
Funding
- National Natural Science Foundation of China [30870439, 30821062]
- Ministry of Science and Technology [2006BAD26B01]
- Chinese Academy of Sciences
- State Key Laboratory of Environmental and Vegetation Change
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Changes in precipitation and nitrogen (N) deposition can influence ecosystem carbon (C) cycling and budget in terrestrial biomes, with consequent feedbacks to climate change. However, little is known about the main and interactive effects of water and N additions on net ecosystem C exchange (NEE). In a temperate steppe of northern China, a field-manipulated experiment was conducted to evaluate the responses of NEE and its components to improve N and water availability from 2005 to 2008. The results showed that both water and N additions stimulated gross ecosystem productivity (GEP), ecosystem respiration (ER), and NEE. Water addition increased GEP by 17%, ER by 24%, and NEE by 11% during the experimental period, whereas N addition increased GEP by 17%, ER by 16%, and NEE by 19%. The main effects of both water and N additions changed with time, with the strongest water stimulation in the dry year and a diminishing N stimulation over time. When water and N were added in combination, there were nonadditive effects of water and N on ecosystem C fluxes, which could be explained by the changes in species composition and the shifts of limiting resources from belowground (water or N) to above-ground (light). The positive water and N additions effects indicate that increasing precipitation and N deposition in the future will favor C sequestration in the temperate steppe. The non-additive effects of water and N on ecosystem C fluxes suggest that multifactor experiments are better able to capture complex interactive processes, thus improving model simulations and projections.
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