Long-term decline in white-tailed deer browse supply: can lichens and litterfall act as alternative food sources that preclude density-dependent feedbacks
Bibliographic record
Abstract
Selective browsing by cervids has persistent impacts on forest ecosystems. On Anticosti Island, Quebec, Canada, introduced white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) have caused massive changes to the native boreal forest. Despite the apparent stability of the deer population over recent decades, we suspected that they were not at equilibrium with their browse supply and that further degradation of the habitat had occurred. A comparison of two browse surveys conducted 25 years apart showed a strong decline in browse availability. Although balsam fir (Abies balsamea (L.) P. Mill.) remained the most available browse species, it declined or disappeared from most stands (n = 13). Preferred deciduous species that were still available 25 years ago have almost disappeared. The continuous decline of the browse supply confirmed our hypothesis. This situation may be exacerbated by a subsidy from the winter litterfall, a significant and stable alternative food source. The abundance of litterfall from mature trees is independent of browsing over a long time period, which introduces a temporal uncoupling between the impact of deer browsing on balsam fir seedlings and the negative feedback from recruitment failure of mature balsam fir on the deer population. This means that the system is susceptible to being forced into an alternative regime.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".