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Record W2116880865 · doi:10.1890/08-0074.1

Modeling the carbon balance of Amazonian rain forests: resolving ecological controls on net ecosystem productivity

2009· article· en· W2116880865 on OpenAlexafffund
R. F. Grant, Lucy R. Hutyra, R. Cde Oliveira, J. William Munger, S. R. Saleska, Steven C. Wofsy

Bibliographic record

VenueEcological Monographs · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Alberta
FundersWestern Canada Research Grid
KeywordsEnvironmental scienceEddy covarianceEcosystemAmazonianEcologyBiomass (ecology)NutrientSeasonalityHydrology (agriculture)Atmospheric sciencesAmazon rainforestBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.205
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2009
Admission routes2
Has abstractyes

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