Impact of light intensity on sugar maple leaf physical traits and consequences for caterpillar preference and performance
Bibliographic record
Abstract
This study investigates the impact of light intensity on the physical defenses of sugar maple (Acer saccharum) saplings and how these defenses affect interactions with insect herbivores. Conducted at the Kenauk Nature Reserve in Quebec, the study involved 12 sites representing both shaded and sunlit environments. Leaf traits, including thickness, specific leaf area (SLA), water content, and toughness, were measured from 216 leaves collected in 2021 and 40 in 2022. To explore herbivore interactions, two Erebid caterpillars including the invasive spongy moth (Lymantria dispar) and the native white-marked tussock moth (Orgyia leucostigma) were used in controlled bioassays that assessed caterpillar feeding preference and performance. The results revealed that sunlit leaves were significantly thicker and had lower SLA compared to shaded leaves across both years. While water content was lower in sunlit leaves in 2021, it showed no significant difference in 2022. Leaf toughness, measured only in 2022, did not significantly differ between sunlit and shaded leaves. Caterpillar feeding preference tests indicated that white-marked tussock moth caterpillars consumed more sunlit leaves, showing a significant preference for them. However, spongy moth caterpillars did not exhibit a significant preference. In terms of performance, white-marked tussock moth caterpillars had a higher survival rate and larger pupal mass on sunlit saplings in 2021, but no significant differences were observed in laboratory feeding initiation assays with first-instar caterpillars in 2022. These findings suggest that a tree's investment in sun leaves to maximise photosynthesis is not necessarily followed by increased defense against herbivory. This research contributes to our understanding of how environmental factors like light can shape plant defense strategies and impact insect herbivore dynamics in temperate forests.
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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 | 0.000 | 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) | 0.001 | 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".