Functional Responses and Resilience of Boreal Forest Ecosystem after Reduction of Deer Density
Bibliographic record
Abstract
The functional trait-based approach is increasingly used to predict responses of ecological communities to disturbances, but most studies target a single taxonomic group. Here, we assessed the resilience of a forest ecosystem to an overabundant herbivore population by assessing changes in 19 functional traits for plant, 13 traits for ground beetle and 16 traits for songbird communities after six years of controlled browsing on Anticosti Island (Quebec, Canada). Our results indicated that plants were more responsive to 6 years of reduced browsing pressure than ground beetles and songbirds. However, co-inertia analysis revealed that ground beetle communities responded in a similar way than plant communities with stronger relationships between plant and ground beetle traits at reduced deer density, a pattern not detected between plant and songbird. High deer density favored plants species that reproduce vegetatively and with abiotic pollination and seed dispersal, traits implying little interaction with animal. On the other hand, traits found at reduced deer density mostly involved trophic interaction. For example, plants in this treatment had fleshy fruits and large seeds dispersed by birds or other animals whereas ground beetle species were carnivorous. Overall, our results suggest that plant communities recovered some functional components to overabundant herbivore populations, since most traits associated with undisturbed forests were reestablished after six years of deer reduction. The re-establishment of functional plant communities with traits involving trophic interaction induces changes in the ground-beetle trait community, but forest structure remains likely insufficiently heterogeneous to shift the songbird trait community within six years.
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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) | 0.000 | 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".