Do physical leaf traits predict insect herbivory? Testing bottom-up pressures in two closely related maple trees in a temperate forest in Quebec
Bibliographic record
Abstract
Closely related plant species often share similar leaf traits, experience the same level of insect herbivore damage, and support identical herbivore communities. The sugar maple ( Acer saccharum) and black maple ( Acer nigrum) provide an ideal system to test hypotheses about drivers of insect herbivory in long-lived forest trees since they are closely related yet differ in leaf physical traits. We tested whether variations in foliar traits such as thickness, toughness, specific leaf area (SLA), and trichome density influence insect herbivore damage, community composition, and feeding behavior on these two closely related trees. Field surveys in two nature reserves over 3 years assessed 10 insect herbivore damage types and measured leaf traits. Results showed consistent differences in leaf traits, with black maples having thicker and tougher leaves with lower SLA and higher trichome density. However, these traits did not significantly correlate with total herbivore damage. The maple species had no significant differences in total herbivore damage or abundance. Laboratory bioassays with two common moth caterpillars revealed no significant differences in preference or survival rate on foliage from either tree species. These findings suggest that while foliar traits differ, they do not offer better defense against insect herbivory in black maples.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".