Nitrogen Turnover in Forest Floors of Coastal Douglas-Fir at Sites Differing in Soil Nitrogen Capital
Bibliographic record
Abstract
Nitrogen cycling is generally considered to be more rapid on sites with high availability of N; this is usually associated with differences in tree species composition. We tested whether N cycling in stands of a single tree species increased with increasing mineral soil nitrogen capital. Rates of N cycling in nine stands of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) were estimated by measuring annual N input in litter, N content of the forest floor, and net N mineralization rate in the forest floor. Rates of C and N turnover were estimated from the litterfall:forest floor ratio. The amount of N returned in litter increased as soil N capital increased. The increase in litter N content resulted from both increased litter mass (C content) and increased N concentration in litter. Despite the greater litter input, forest floor mass was smaller at N-rich sites, indicating more rapid turnover of the forest floor on N-rich sites. There was a positive relationship between fractional annual loss of N from the forest floor and soil N capital. Therefore, even without changes in tree species composition, sites with greater soil N capital returned more N in annual litterfall and had faster turnover of N in the forest floor. Fractional annual loss of C also increased with increasing soil N capital, indicating faster decomposition on N-rich sites. The rate of net N mineralization during laboratory incubations of the forest floor was not correlated with soil N capital or N concentrations of litter, but was related to the C:N ratio of the forest floor. Net N mineralization was appreciable only at two sites where forest floor C:N ratios were <35. The rate of net N mineralization or C:N ratio of the forest floor were not good indicators of N availability at these sites. The results of this study are consistent with the hypothesis that rates of N cycling are faster on N-rich sites, and that this effect can occur in the absence of changes in tree species composition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".