Insect Defoliators in Recovering Industrial Landscapes: Effects of Landscape Degradation and Remediation Near an Abandoned Metal Smelter on Gypsy Moth (Lepidoptera: Lymantriidae) Feeding, Frass Production, and Frass Properties
Bibliographic record
Abstract
Although insect defoliators are recognized as major agents of ecological change in North American forests, their ecology in industrially degraded landscapes with poor-quality soils, metal contamination, and marginal vegetation growth is largely unknown. We fed gypsy moth larvae (Lymantria dispar L.) paper birch leaves (Betula papyrifera Marsh) (Fagales: Betulaceae) collected from four forested catchment areas near an abandoned Cu/Ni smelter in Sudbury (Ontario, Canada) with different histories of industrial degradation and remediation (reference, remediated, natural recovery, and degraded). We measured caterpillar feeding, frass properties and decomposability, and the effects of frass on the growth of ticklegrass (Agrostis scabra Willd.) (Poales: Poaceae). Caterpillars generally ate more (+25-50%) and produced more frass (+30-40 %) on a diet of leaves from the more industrially degraded sites. Frass had an overall positive effect on plant survivorship (+4.1-10.8 effect size) and growth (+0.1-0.5 effect size), although the smallest benefits came from frass derived from vegetation from the more heavily degraded sites. Our results suggest that defoliating insects respond to differences in environmental degradation and remediation and that industrial landscapes may be particularly susceptible to more extensive defoliation and increased conversion of foliar biomass into frass, which could alter plant growth and survivorship, soil development, and nutrient and metal cycling. Some of these effects may pose additional challenges to landscape recovery (e.g., increased defoliation) while others may be beneficial (e.g., enhanced plant growth and soil development).
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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) | 0.000 | 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) | 0.000 | 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".