A continental comparison indicates long-term effects of forest management on understory diversity in coniferous forests<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.
Bibliographic record
Abstract
Promotion of species diversity has become a major goal in forestry. This requires an understanding of the impacts of management disturbance on species diversity relative to natural drivers such as climatic or edaphic conditions on the relevant temporal scales, i.e., centuries. We examined the effects of long-term management disturbance on understory plant diversity in coniferous forests by comparing structure types (ages since disturbance) between regions with comparable abiotic settings but contrasting management history, i.e., management for centuries in central Europe versus the first logging in primary forests in western Canada. We systematically sampled three age classes after disturbance and compared their alpha diversity and species composition. The structure types (age classes) showed similar differences in alpha diversity in both landscapes, while the response of species compositions differed between the two. Fewer late-successional specialists occurred in the European landscape. Within the setting of our study, the structure types, which reflect the time since major forest management disturbance, affected understory species richness and composition at least as strongly as environmental conditions such as climate, soil, and tree layer diversity across the broad altitudinal gradients that we sampled. Our results suggest that forest management affects the diversity of coniferous forests, with management for centuries disadvantaging late-successional specialists. Furthermore, it appears that human action is becoming the major determinant of diversity of coniferous forests, emphasizing the need for sustainable management schemes.
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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 | 0.000 | 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 | 0.000 |
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".