Heavy browsing by a mammalian herbivore does not affect fluctuating asymmetry of its food plants
Bibliographic record
Abstract
The only 3 published studies relating vertebrate herbivores to plant fluctuating asymmetry (FA) found significant correlations between grazing intensity and plant FA. The general value of these early findings is unclear, however, because FA studies are sensitive to selective reporting, the tendency to publish only a subset of studies that were undertaken. From 2000 to 2003 we quantified the correlations between past herbivory and plant FA in 3 plant–herbivore systems centred on a single mammal species, the North American porcupine (Erethizon dorsatum). We measured leaf FA in pairs of paper birch (Betula papyriferae; n = 24 pairs), quaking aspen ( Populus tremuloides; n = 25 pairs), and jack pine ( Pinus banksiana; n = 15 pairs) trees each containing a control (uneaten) and test (eaten) tree. Although damage incurred by trees from porcupine browsing was severe, we found no statistical association between plant FA and herbivory. We obtained this finding even though our study design did capture subtle variations in plant FA associated to plant genotype or year of sampling. Our study contrasts with earlier findings that plant FA is related to herbivory pressure. There may have been a publication bias as a result of selective reporting in this field of research. Therefore, replication (same hypothesis, same study system) and quasireplication (same hypothesis, different study system) are particularly important.
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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 | 0.002 |
| 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".