Increased per capita herbivory in the shade: Necessity, feedback, or luxury consumption?
Bibliographic record
Abstract
Leaf chemistry and physiology vary with light environment and are often thought to directly affect herbivory patterns. Biotic (e.g., parasitoids and predators) and abiotic (e.g., temperature, relative humidity) factors known to influence herbivory also co-vary with light environment. Irrespective of mechanism, light-based differences in herbivore damage must be the result of variable herbivore abundance, per capita effects, or both. We examined the effect of light environment on leaf defence and leaf nutritional quality in Lindera benzoin (Lauraceae) and relate this to the abundance and impact of its lepidopteran herbivore Epimecis hortaria (Lepidoptera: Geometridae). In this system we consistently observe greater natural field herbivory in shade habitats relative to high light habitats, despite similar herbivore abundances; differences in herbivory are therefore most likely attributable to different per capita impacts of herbivores across environments. Potential herbivore behaviours responsible for the observed field pattern include increased foraging per day and longer developmental periods in shade habitats. A more complete understanding of observed herbivory patterns requires incorporating variation in herbivore behaviour as influenced by abiotic or biotic factors that co-vary with the different light environments.
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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 | 0.001 |
| 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 | 0.001 |
| 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".