Long-term effects of herbivory on tree growth are not consistent with browsing preferences
Bibliographic record
Abstract
Herbivores affect plants via direct consumption, but also indirectly via changes in the vegetation. These indirect effects may only manifest after decades of exposure and, as a result, their impact is rarely accounted for. To better understand the direct and indirect impacts of a large herbivore, moose ( Alces alces), on forests, we measured tree growth in areas that were both subjected to and excluded from herbivory for over 80 years. Growth data were gathered from five tree species, ranging from low to high palatability. We found that at small sizes, Betula papyrifera, a preferred species, benefited from herbivore exclusion. However, larger individuals grew more when exposed to herbivory, a response we attribute to lower competition in heavily browsed conditions. Populus tremuloides, a highly preferred tree, did not show any differences between levels of herbivory. Abies balsamea, a preferred winter browsed, was only marginally affected by browsing at smaller sizes. The two non-preferred species, Picea glauca and Picea mariana, did not show differential growth between herbivory levels. We conclude that herbivores can impact forests through both direct and indirect effects, that these effects are size specific, and that effects vary among species in ways not always predicted by consumption patterns.
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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 | 0.000 | 0.000 |
| 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".