Brown bears (<i>Ursus arctos</i>) as ecological engineers: the prospective role of trees damaged by bears in forest ecosystems
Bibliographic record
Abstract
Brown bears (Ursus arctos L., 1758) damage trees (mainly the cambium) during feeding and marking. It is thought that their stripping of bark and their subsequent foraging on sapwood can lead to substantial economic losses. However, the part played by this process in the ecosystem is still unknown. We hypothesize that brown bear foraging makes resources and habitats available to other species. We anticipated that wounds perpetrated by bears during foraging would attract saproxylic insects and consequently insectivorous birds. Bear wounds were searched for on trees in the Bieszczady Mountains (Polish Eastern Carpathians) from 2008 to 2011. We analyzed 278 wounds in silver firs (Abies alba Mill.) of different age classes: 43% of them had holes made by wood-boring insects and 33% had signs of woodpecker feeding. The presence of insect holes and woodpecker marks in the wound was found to depend on wound area, wound age, and tree circumference (insects only). In particular, the oldest wound age class (>5 years old) was associated with a high probability of occurrence of insects and woodpeckers. Insects were attracted by wounds of smaller area than woodpeckers. The density of insect holes (number of wood-boring insects) depended on the wound age and tree circumference. Insect densities in fresh wounds (<1 year old) were significantly lower than in the older age classes. The results show that bear-made wounds provide breeding and feeding sites for both insects and birds. Especially for woodpeckers (forest indicator species) and for saproxylic insects (mainly endangered species), brown bears may play an important role as niche constructors and ecological engineers. Therefore, it would be advisable to leave a considerable number of bear-wounded trees in the forest ecosystems for conservation purposes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".