Data from: Recovery of a boreal ground-beetle (Coleoptera: Carabidae) fauna 15 years after variable retention harvest
Bibliographic record
Abstract
1. Retention harvests are preferred over traditional clear-cuts for sustainable forest management because maintenance and re-establishment of native forest biodiversity is a priority. However, few studies have examined long-term responses of biotic assemblages to retention harvest at particular sites. 2. We studied the effects of decreasing initial harvest intensities (clear-cut, 10, 20, 50, and 75% dispersed green tree retention) on carabid beetle assemblages relative to assemblage changes in un-harvested control stands in four successionally ordered cover-types of boreal mixedwood forest. We also studied temporal effects by comparing assemblages over a 16-year pre- and post-harvest period, using data collected through monitoring of the EMEND (Ecosystem Management Emulating Natural Disturbance) experiment in NW Alberta, Canada. 3. Retention harvests affected assemblages differently across cover-types. Assemblages in compartments harvested in the earlier forest successional stages of ‘deciduous’ or ‘deciduous with spruce understory’ converged toward the pre-harvest structure of corresponding controls over time. In contrast, beetle assemblages in ‘mixed’ or ‘conifer’ compartments, that represent later successional forest, moved steadily away from their pre-harvest structures during the first post-harvest decade. These latter assemblages became strikingly more similar to those under deciduous canopies by 15 years post-harvest. Synthesis and applications. Variable retention harvests will promote and maintain biodiversity better than clear-cutting. Higher retention levels promote faster recovery, but toward fauna typical of early successional forest in all cover-types. Carabids associated with conifer habitats are less resistant to impact from harvesting than are those from broadleaf deciduous forest. Therefore, conifer dominated stands present the most significant management challenge and higher retention levels are required to promote rapid and effective faunal recovery in such late successional stands.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".