The effects of variable retention forestry on coarse woody debris dynamics and concomitant impacts on American marten habitat after 27 years
Bibliographic record
Abstract
Coarse woody debris (CWD) in the form of logs, downed wood, stumps and large tree limbs is an important structural habitat feature for many small mammal species, including the American marten (Martes americana). At a long-term experimental trial in northern temperate hemlock-cedar forests of British Columbia, Canada, we analysed the impact of varying amounts of overstory basal area retention: 0% (clearcut), 40%, 70%, and 100% (unharvested) on CWD volume, decay class, and inputs from windthrow over 27 years. We used CWD attributes (diameter, length, decay class, and height above the ground) known to be favourable for martens to create an index for assessing the impact of harvesting intensity on CWD habitat features. Stands with 70% retention had CWD attributes that resulted in CWD habitat features similar to unharvested stands. Clearcuts contained pieces that were smaller, more decayed, and closer to the ground, which contributed to a habitat that was less valuable, compared with stands that had higher retention. Over the 27-year period, windthrown trees were the majority of CWD inputs, and volume change was positively related to percent retention. Our results highlight that forest management influences CWD size and input dynamics over multiple decades, and the need for consideration of these impacts when undertaking long-term multiple-use forestry planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".