Succession of bryophyte assemblages following clear-cut logging in boreal spruce-dominated forests in south-central Sweden — Does retrogressive succession occur?
Bibliographic record
Abstract
The recovery process of boreal bryophyte communities after clear-cutting was studied in a chronosequence in south-central Sweden. We hypothesized that high initial grass cover on clearcuts, high litter cover and low light levels during canopy closure, and shortage of coarse woody substrates would constrain recovery in different ways. Instead, both epigeic and epixylic guilds (i.e., species growing on forest floor and deadwood) displayed a gradual increase in similarity over time from the clear-cut phase, perhaps because of the absence of distinct peaks in needle litter and canopy cover. Epixylic species started to recover long before the accumulation of deadwood, indicating that microclimate rather than substrate availability was the most constraining factor during the first 50 years. Since we did not find any other bottlenecks during the succession after clear-cutting, conservation measures aiming at decreasing local extinction rates during clear-cutting may also increase long-term persistence. On the other hand, as the results from the epixylic guild suggest, other factors during the forest succession, such as the development of a suitable microclimate, might be more important for some organisms, thus possibly mitigating such long-term positive effects of adjusted management during the clear-cutting operation.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".