Data from: Combining aggregated and dispersed tree retention harvesting for conservation of vascular plant communities
Bibliographic record
Abstract
Retention harvesting (also called tree retention or structural retention), in which live mature trees are selectively retained within harvested stands at different retention levels and in different patterns (aggregated to dispersed), is increasingly being used to mitigate the negative impacts of forest harvesting on biodiversity. However, the effectiveness of combining different patterns of retention harvesting for conservation and recovery of understory vascular plants in the long-term is largely unknown. To address this gap, we compared understory vascular plant diversity, abundance, and composition between aggregated retention and five levels of surrounding dispersed retention (0% = clearcut, 10%, 20%, 50%, 75%) 15 years post-harvest. We also investigated the influence of dispersed retention on the ability of embedded retention patches to support plant communities characteristic of unharvested forests, and whether it varies by patch size of aggregated retention (0.20 ha or 0.46 ha) and position within patches (edge or interior). Species richness, diversity, and cover were higher in the dispersed retention than in the patch retention as the harvested areas favored early-seral plant species. Graminoid cover was greater at the edges than in the interior of large patches. Retention patches as small as 0.2 ha more effectively supported shade-tolerant (forest interior) plant communities when they were surrounded by higher levels of dispersed retention (as compared to patches retained within clearcuts). Overall, the combined use of both aggregated and dispersed retention within a given cutblock benefits both late- and early-seral plant species and thus could effectively conserve understory plant assemblages in harvested landscapes. Sustainable forest management should therefore consider using a range of retention patch sizes combined with varying levels of surrounding dispersed retention in harvest designs to achieve objectives for plant conservation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".