Spatiotemporal dynamics of natural tree regeneration in unmanaged subalpine conifer forests with high wild ungulate densities
Bibliographic record
Abstract
Increasing wild ungulate populations in the northern hemisphere led to extensive research on their influence on forest regeneration. According to the results of different studies, high ungulate densities may alter forest structure and composition, as well as inhibit forest regeneration. However, little is known about the long-term impact of large herbivores on forest dynamics at the landscape scale. Therefore, we studied forest regeneration in conifer forests of the highly protected Swiss National Park. We assessed sapling densities and apical shoot loss by repeated systematic sampling in 2003 and in 2011–2012 in two valleys featuring exceptionally high densities of wild ungulates of up to 86 individuals per vegetation-covered square kilometre during summer and 28 individuals per vegetation-covered square kilometre during winter. Mean apical shoot loss ranged between 2.9% and 10.6% depending on study site and assessment year. In addition to the increasing sapling densities, these results suggest that ungulates had little impact on tree establishment in the studied subalpine conifer forests. Additionally, sapling diversity was higher on plots repeatedly influenced by ungulates (apical shoot loss), suggesting rather positive effects of ungulates on sapling diversity. Habitat containing a mixture of forest and grassland without livestock or human disturbance provides a high-quality environment for ungulates, which likely explains the low impact of ungulates on forest regeneration in our study area.
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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 | 0.000 | 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".