Vertical stratification of leaf physical traits exerts bottom-up pressures on insect herbivory in a sugar maple temperate forest
Bibliographic record
Abstract
Do vertical gradients structure temperate forest insect herbivore communities? We tested the hypothesis that the increase in light intensity from understory to forest canopy level drives differences in leaf physical traits and budburst phenology that impact insect herbivores and thus play a role in structuring both herbivore communities and the damage they cause to trees. Twelve sugar maple (Acer saccharum) trees were monitored in southern Quebec, examining herbivore patterns from understory to canopy. Three sampling sessions took place in the summers of 2020, 2021, and 2022, recording temperature, humidity, sun exposure, and leaf physical traits in three strata. In the first two years, we measured herbivory rates, quantifying affected leaf surface percentage by damage type. Overall, herbivory damage decreased from the understory to the shade canopy and sun canopy in 2020, driven by leaf cutters and skeletonizers. Leaf stipplers and blotch miners also followed this pattern in 2020. The 2021 sampling showed a similar, albeit weaker, pattern. Leaf cutters and skeletonizers consistently caused less damage with increasing height in the canopy. The abundance of insect herbivores collected in 2022 matched the observed damage trend. Leaf thickness increased along the vertical gradient, making leaves less accessible to herbivores. Variation in plant traits according to sun exposure thus contributes to explaining vertical stratification of insect herbivore damage. The average annual herbivory rate of 9.1% of leaf surface suggests limited evidence supporting an important contribution of background herbivory to the decline of sugar maple 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".