Changes in net ecosystem productivity with forest age following clearcutting of a coastal Douglas-fir forest: testing a mathematical model with eddy covariance measurements along a forest chronosequence
Bibliographic record
Abstract
We hypothesized that changes in net ecosystem productivity (NEP) during aging of coastal Douglas-fir (Pseudotsuga menziesii Mirb. Franco) stands could be explained by (1) changing nutrient uptake caused by different time scales for decomposition of fine, non-woody and coarse woody litter left after harvesting, (2) declines in canopy water status with lengthening of the water uptake pathway during bole and branch growth, and (3) increases in the ratio of autotrophic respiration (R (a)) to gross primary productivity (GPP) with phytomass accumulation. These hypotheses were implemented and tested in the mathematical model ecosys against eddy covariance (EC) measurements of forest CO(2) and energy exchange in a post-clearcut Douglas-fir chronosequence. Hypothesis 1 explained how (a) an initial rise in GPP observed during the first 3 years after clearcutting could be caused by nutrient mineralization from rapid decomposition of fine, non-woody litter with lower C:N ratios (assart effect), (b) a slower rise in GPP during the next 20 years could be caused by immobilization during later decomposition of coarse woody litter, and (c) a rapid rise in GPP between 20 and 40 years after clearcutting could be caused by nutrient mineralization with further decomposition of coarse woody litter and of its decomposition products. During periods (a) and (b), heterotrophic respiration (R (h)) from decomposition of fine and coarse litter greatly exceeded net primary productivity (NPP = GPP - R (a)) so that Douglas-fir stands were large sources of CO(2). During period (c), NPP exceeded R (h) so that these stands became large sinks for CO(2). Hypothesis 2 explained how declines in NPP during later growth in period (c) could be caused by lower hydraulic conductances in taller trees that would force lower canopy water potentials and hence greater sensitivity of stomatal conductances and CO(2) uptake to vapor pressure deficits. Enhanced sensitivity to vapor pressure deficits was also apparent in the EC measurements over the post-clearcut chronosequence. Hypothesis 3 did not contribute to the explanation of forest age effects on NEP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".