Causes and consequences of deer browsing on red trillium (<i>Trillium erectum</i>) along an elevational gradient
Bibliographic record
Abstract
Models of ecological responses to climate warming predict species’ migration towards higher latitudes or elevations. However, models often neglect nonclimatic factors, such as herbivory, which could slow down or prevent geographic range expansion. A previous study in Mont Mégantic National Park (Quebec) found that in one year (2016), browsing by white-tailed deer on Trillium erectum L. was substantially higher at high elevations than low elevations. Under the hypothesis that deer herbivory could limit the upper elevational range expansion of T. erectum, here we ask (i) whether herbivory increased with elevation during two additional years (2017 and 2018), (ii) whether the rate of deer visitation increases with elevation, and (iii) whether the effect of herbivory on relative growth rates increases with elevation. Contrary to the earlier study, we did not find a significant trend of herbivory with elevation, although there was a weak positive relationship between deer visitation and elevation. We found a strong negative impact of browsing on relative growth rates, but the magnitude of this effect did not vary with elevation. Our results thus do not support the hypothesis that herbivory limits the range of T. erectum at high elevation, but herbivory could have a negative impact on populations in general if the browsing rate remains high.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".