Downed woody debris varies with climate and harvesting treatment in Douglas-fir forests of British Columbia, Canada
Bibliographic record
Abstract
Downed woody debris is important for biodiversity, forest regeneration, and carbon, nutrient, and water cycling, and past studies have examined how the coarse fraction is affected by climate or harvesting. In a field study in Douglas-fir dominated forests, we expand existing knowledge by investigating the interacting effects of climate and harvesting on downed woody debris of all sizes. Across a 900-km long latitudinal gradient in British Columbia, we found that coarse woody debris (CWD, >7.5 cm diameter) in humid climates contained 700% greater carbon stocks, had 500% greater volume, and was more diverse than in arid climates. Pre- and post-harvest, small and fine woody debris comprised a higher proportion of total woody debris carbon stocks in arid than moist climates, especially after clearcutting and seed tree treatments. Harvesting generally decreased total CWD volume, but it was not depleted on any site. Harvesting substantially reduced the volume of large, highly decomposed CWD except at the two most arid sites, and losses of large CWD increased with increasing tree removal. These losses were accompanied by a pulse of fresh, small diameter CWD and SWD which are short-term organic nutrient sources but have less habitat value than larger pieces and contribute to fuel loads. Because CWD was less abundant in arid than humid mature forests, care must be taken on arid sites to avoid its depletion during harvesting, especially clearcutting, where future woody debris inputs will not occur for decades.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".