Compositional and functional trajectories of herbaceous communities after deer density control in clear-cut boreal forests
Bibliographic record
Abstract
Overabundant populations of large herbivores have strong persistent effects on forest composition, structure, and function. However, the mechanism through which plant communities recover their original composition and function after herbivore management remains poorly understood. We assessed the temporal trajectories of the herbaceous communities in Abies balsamea (L.) Mill. and Picea glauca (Moench) Voss stands on Anticosti Island (Quebec, Canada) over 8 years following clear-cutting and deer management. The impact of deer exclusion or reduction to 7.5 and 15 deer·km–2 was compared with benchmark in situ deer densities (27 and 56 deer·km–2). Effects of deer management treatments on plant species and functional trait assemblages over time were assessed using principal response curves. Although complete deer exclusion seemed necessary to modify species composition from that occurring under intense browsing, a reduced density of 7.5 deer·km–2 was sufficient to induce significant changes in functional trait assemblages of regenerating stands. For instance, reduced deer densities favored plants with brightly colored flowers and compound inflorescences pollinated by animals and producing large seeds and fleshy fruits dispersed by animals. We conclude that the boreal forest’s herbaceous communities are resilient to chronic browsing when deer population reduction and forest clearcutting are applied in synergy.
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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 | 0.000 | 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) | 0.000 | 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".