Dynamics and recovery of forest understory biodiversity over 17 years following varying levels of retention harvesting
Bibliographic record
Abstract
Abstract Retention harvesting is advocated as an alternative to intensive timber harvesting, such as clear‐cutting, to better maintain or facilitate recovery of biodiversity and other ecological values in managed forests. However, it is not clear how long the benefits of retention harvests persist. We investigated responses of understory vascular plant cover, richness, diversity (inverse Simpson index) and composition to a gradient in dispersed retention (2% [clear‐ cut], 10%, 20%, 50% and 75% retention; unharvested reference [100% retention]) at 3, 6, 11, and 17 years after harvest, in four boreal mixedwood forest types (deciduous (broadleaf)‐dominated, deciduous‐dominated with conifer understory, mixed, and conifer‐dominated) in western Canada. Understory cover and richness tended to increase in the short‐term (3 years), peaked at 6–11 years with differences following the gradient of harvesting intensity, then plateaued or declined in the second decade (17 years), by which time there were minimal or no differences among harvesting levels, including the reference. Responses for diversity were minimal. In contrast, composition varied along the gradient of harvesting intensity and showed little recovery towards the unharvested condition over the 17‐year period. Generally, for plant community composition, clear‐cut and lower retention treatments (10%, 20%) were similar to one another but differed from the higher retention and unharvested reference treatments. Synthesis and applications: Retention harvests can moderate the negative impacts of harvesting and facilitate the recovery of biodiversity. Our results suggest that for the cover, richness and diversity of understory vascular plants, this moderating influence is weak and short‐lived. However, higher levels of retention can temper changes in understory composition relative to the unharvested forest, but full recovery is likely to be slow and will be complicated by post‐harvest regeneration dynamics.
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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 | 0.000 | 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) | 0.001 | 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".