Abstract
There is still much uncertainty about ecological controls on the rate and direction of net CO2 exchange by tropical rain forests, in spite of their importance to global C cycling. These controls are thought to arise from hydrologic and nutrient constraints to CO2 fixation caused by seasonality of precipitation and adverse chemical properties of some major tropical soil types. Using the ecosystem model ecosys, we show that water uptake to a depth of 8 m avoids constraints to CO2 and energy exchange from soil drying during five-month dry seasons typical for eastern Amazonian forests. This avoidance in the model was tested with eddy covariance (EC) measurements of CO2 and energy fluxes during 2003 and 2004 over an old-growth forest on an acidic, nutrient-poor oxisol in the Tapajós National Forest (TNF) in Pará, Brazil. Modeled CO2 fixation was strongly constrained by slow phosphorus (P) uptake caused by low soil pH. Daytime CO2 influxes in the model were in close agreement with EC measurements (R2 > 0.8) during both wet and dry seasons. Both modeled and measured fluxes indicated that seasonality of precipitation affected CO2 and energy exchange more through its effect on radiation and air temperature than on soil water content. When aggregated to a yearly scale, modeled and gap-filled EC CO2 fluxes indicated that old-growth forest stands in the TNF remained within 100 g C·m-2·yr -1 of C neutrality in the absence of major disturbance. Annual C transformations in ecosys were further corroborated by extensive biometric measurements taken in the TNF and elsewhere in the Amazon basin, which also indicated that old-growth forests were either small C sources or small C sinks. Long-term model runs suggested that rain forests could be substantial C sinks for several decades while regenerating after stand-replacing disturbances, but would gradually decline toward C neutrality thereafter. The time course of net ecosystem productivity (NEP) in the model depended upon annual rates of herbivory and tree mortality, which were based on site observations as affected by weather (e.g., El Niño Southern Oscillation [ENSO] events). This dependence suggests that rain forest NEP is strongly controlled by disturbance as well as by weather.
Original language | English (US) |
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Pages (from-to) | 445-463 |
Number of pages | 19 |
Journal | Ecological Monographs |
Volume | 79 |
Issue number | 3 |
DOIs | |
State | Published - Aug 2009 |
Keywords
- Brazil
- CO exchange
- Disturbance
- Ecosys
- Ecosystem modeling
- Net ecosystem productivity
- Rain forest
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
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Modeling the carbon balance of Amazonian rain forests: resolving ecological controls on net ecosystem productivity
Grant, R. F. (Contributor), Hutyra, L. R. (Contributor), Munger, J. W. (Contributor), Saleska, S. R. (Contributor) & Wofsy, S. C. (Contributor), figshare, 2016
DOI: 10.6084/m9.figshare.c.3309468.v1, https://figshare.com/collections/Modeling_the_carbon_balance_of_Amazonian_rain_forests_resolving_ecological_controls_on_net_ecosystem_productivity/3309468/1
Dataset
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Appendix B. Key governing equations for simulating net ecosystem productivity in ecosys: plant water relations.
Grant, R. F. (Contributor), Hutyra, L. R. (Contributor), Munger, J. W. (Contributor), Saleska, S. R. (Contributor) & Wofsy, S. C. (Contributor), figshare, 2016
DOI: 10.6084/m9.figshare.3566733, https://wiley.figshare.com/articles/dataset/Appendix_B_Key_governing_equations_for_simulating_net_ecosystem_productivity_in_ecosys_plant_water_relations_/3566733
Dataset
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Appendix C. Key governing equations for simulating net ecosystem productivity in ecosys: gross primary productivity and autotrophic respiration.
Grant, R. F. (Contributor), Hutyra, L. R. (Contributor), Munger, J. W. (Contributor), Saleska, S. R. (Contributor) & Wofsy, S. C. (Contributor), figshare, 2016
DOI: 10.6084/m9.figshare.3566730, https://wiley.figshare.com/articles/dataset/Appendix_C_Key_governing_equations_for_simulating_net_ecosystem_productivity_in_ecosys_gross_primary_productivity_and_autotrophic_respiration_/3566730
Dataset