Deer browsing and soil disturbance induce cascading effects on plant communities: a multilevel path analysis
Bibliographic record
Abstract
Understanding how large herbivores shape plant diversity patterns is an important challenge in community ecology, especially because many ungulate populations in the northern hemisphere have recently expanded. Because species within plant communities can exhibit strong interactions (e.g., competition, facilitation), selective foraging by large herbivores is likely not only to affect the abundance of palatable species, but also to induce cascading effects across entire plant communities. To investigate these possibilities, we first tested the effects of deer browsing and soil disturbance on herbaceous plant diversity patterns in boreal forest, using standard analyses of variance. Second, we evaluated direct and indirect effects of deer browsing and soil disturbance on the small-scale richness of herbaceous taxa using a multilevel path analysis approach. The first set of analyses showed that deer browsing and soil disturbance influenced herb richness. Path analyses revealed that deer browsing and soil disturbance influenced richness via complex chains of interactions, involving dominant (i.e., the most abundant) browsing-tolerant (DBT) taxa and white birch (Betula papyrifera), a species highly preferred by white-tailed deer (Odocoileus virginianus). We found no evidence that an increase of white birch in fenced quadrats was the direct cause of a decrease in herb richness. However, we found strong evidence that a higher abundance of DBT taxa (i.e., graminoids and Circium arvense), both in fenced and unfenced quadrats, increased herb layer richness. We propose an empirical model in which competitive interactions between white birch and DBT taxa regulate the strength of facilitative relationships between the abundance of DBT taxa and herb richness. In this model, deer browsing and the intensity of soil disturbance initiate a complex chain of cascading effects in boreal plant communities by controlling the abundance of white birch.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".