Generalist predation on forest tent caterpillar varies with forest stand composition: an experimental study across multiple life stages
Bibliographic record
Abstract
1. Generalist enemies can regulate low‐density forest insect populations, and are widely considered to cause greater mortality in more diverse habitats. Forest tent caterpillars ( Malacosoma disstria Hübner; FTC) are a major defoliator of aspen ( Populus tremuloides Micheaux) in the boreal forest, a region with a mosaic of forest stand types. This heterogeneity may influence FTC outbreaks if generalist predation or parasitism differs among stands of different tree composition. 2. Using exclusion experiments we estimate predation and parasitism of FTC across multiple life‐history stages in low‐density populations occupying both aspen (low diversity) and mixedwood stands (high diversity). 3. Arthropod and avian generalist predators were responsible for most natural enemy‐caused mortality of immature FTC, but their relative impacts varied among FTC life‐history stages. Contrary to expectation, predation on late instar larvae and pupae was higher in the less diverse aspen stands and early instar mortality did not differ. 4. By considering multiple life‐history stages, our results provide a more comprehensive view of natural enemy‐caused morality of immature FTC. Because generalist predation on FTC was higher in aspen than in mixedwood stands, we suggest that FTC populations may be slower to reach outbreak levels in aspen stands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".