Modeling analysis of primary controls on net ecosystem productivity of seven boreal and temperate coniferous forests across a continental transect
Bibliographic record
Abstract
Abstract Process‐based models are effective tools to synthesize and/or extrapolate measured carbon (C) exchanges from individual sites to large scales. In this study, we used a C‐ and nitrogen (N)‐cycle coupled ecosystem model named CN‐CLASS (Carbon Nitrogen‐Canadian Land Surface Scheme) to study the role of primary climatic controls and site‐specific C stocks on the net ecosystem productivity (NEP) of seven intermediate‐aged to mature coniferous forest sites across an east–west continental transect in Canada. The model was parameterized using a common set of parameters, except for two used in empirical canopy conductance–assimilation, and leaf area–sapwood relationships, and then validated using observed eddy covariance flux data. Leaf Rubisco‐N dynamics that are associated with soil–plant N cycling, and depend on canopy temperature, enabled the model to simulate site‐specific gross ecosystem productivity (GEP) reasonably well for all seven sites. Overall GEP simulations had relatively smaller differences compared with observations vs. ecosystem respiration (RE), which was the sum of many plant and soil components with larger variability and/or uncertainty associated with them. Both observed and simulated data showed that, on an annual basis, boreal forest sites were either carbon‐neutral or a weak C sink, ranging from 30 to 180 g C m−2 yr−1; while temperate forests were either a medium or strong C sink, ranging from 150 to 500 g C m−2 yr−1, depending on forest age and climatic regime. Model sensitivity tests illustrated that air temperature, among climate variables, and aboveground biomass, among major C stocks, were dominant factors impacting annual NEP. Vegetation biomass effects on annual GEP, RE and NEP showed similar patterns of variability at four boreal and three temperate forests. Air temperature showed different impacts on GEP and RE, and the response varied considerably from site to site. Higher solar radiation enhanced GEP, while precipitation differences had a minor effect. Magnitude of forest litter content and soil organic matter (SOM) affected RE. SOM also affected GEP, but only at low levels of SOM, because of low N mineralization that limited soil nutrient (N) availability. The results of this study will help to evaluate the impact of future climatic changes and/or forest C stock variations on C uptake and loss in forest ecosystems growing in diverse environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".