Pervasive moose browsing in boreal forests alters successional trajectories by severely suppressing keystone species
Bibliographic record
Abstract
Abstract Large herbivores can shape young forest stands and determine the successional trajectory of forested ecosystems by selectively browsing palatable species at the sapling stage. Moose (Alces alces) is the dominant vertebrate herbivore in Fennoscandian boreal forests, and high population densities have raised concerns about potential negative effects on ecosystem functioning and properties including biological diversity and timber production. We used 31 herbivore exclosures in Norway to investigate how forests developed after clear‐cutting with or without moose present. We tested how tree demography, abundances of understory plant functional groups, community composition, and plant diversity (including bryophytes) across multiple scales varied with moose exclusion. After seven years, the exclosures were dominated by deciduous trees, including many large rowan (Sorbus aucuparia) individuals, a functionally important keystone species. In contrast, the open plots subject to moose impacts (browsing, trampling, defecation) were dominated by economically important coniferous trees and there was next to no rowan recruitment to taller height classes. The biomass of large herbs and ferns was much greater inside exclosures. This study emphasizes the large immediate effect of moose on early successional boreal forest stands. Landscape‐level alterations caused by reduced deciduous dominance, and a reduction in large flowering herbs is likely to lead to cascading effects on ecosystem functioning. The management of boreal production forests needs to account for the combined effects of silvicultural practices and ungulate herbivory to ensure ecosystem functioning, but this management goal may be jeopardized in our study regions due to drastically reduced abundance of keystone species.
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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".