What Drives Insect Herbivory Patterns in A Sugar Maple Temperate Forest? Bottom-Up and Top-Down Pressures on Insect Defoliators Within and Between Maple Trees
Bibliographic record
Abstract
Insect herbivory plays a vital role in forest ecosystems, structuring trophic webs and maintaining biodiversity. While dramatic insect outbreaks often dominate attention, low intensity but persistent background herbivory also contributes to ecological processes. This thesis investigates the ecological drivers of background insect herbivory in sugar maple-dominated forests, focusing on within-tree variations, between-tree diversity, and trophic interactions. Using the "green world" hypothesis as a framework, this study examines how bottom-up and top-down forces shape herbivore populations. Bottom-up pressures from leaf physical traits varied with vertical stratification and light gradients: sunlit canopy leaves exhibited higher thickness, lower specific leaf area (SLA), and lower water content with less herbivory damage, whereas shaded understory leaves showed lower thickness, higher SLA and greater water content with increased herbivory damage. Paradoxically, feeding bioassays revealed that a local lepidopteran herbivore preferred sun-exposed leaves over shaded leaves and had a better performance on sun-exposed leaves. These findings underscore the complex interplay between leaf traits and herbivore behavior, indicating that physical defenses alone cannot fully explain observed patterns of herbivory. Top-down forces, including predation and parasitism, were further studied. Predation rates varied across vertical gradients but not between saplings under different light conditions, with arthropods dominating shaded understories and birds in sunlit canopies. While higher bird predation may contribute to reduced herbivory in the sun canopy, limited parasitoids data prevented robust conclusions about vertical variation of natural enemies. These spatially variable top-down pressures on herbivore populations deserves further attention. Comparisons between sugar maple and black maple (Acer nigrum) revealed key differences in leaf traits, like tougher leaves and denser trichomes in black maples, but no difference in herbivore communities. This suggests that despite notable trait differences, insect herbivores did not distinguish between the two tree species, likely reflecting their close evolutionary relationship. By integrating within-tree and between-tree variations with trophic interactions, this thesis provides a comprehensive view of insect herbivory regulation in sugar maple forests. These results refine our understanding of the green world hypothesis by highlighting how spatial variation in plant traits and predator communities shapes herbivore populations. This insight enhances ecological theory and informs forest management strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".