Phenotypic variation in food utilization in an outbreak insect herbivore
Bibliographic record
Abstract
The effects of nutrition may have subtantial impact on insect evolution by shaping different components of phenotypes. The key to undestanding this evolutionary process is to know how nutritional condition affects additive and nonadditive components of the phenotype. However, this is poorly understood in outbreaking insects. We investigated the additive and nonadditive variation present in food utilization traits in spruce budworm individuals subjected to chronic nutritional stress. A total of 160 full-sib families of spruce budworm (Choristoneura fumiferana Clem.) were raised under laboratory conditions, feeding on 2 diets (high and low energy) during 3 generations. Variables tested were pupal mass, consumption rate (RCR), growth rate (RGR), approximate digestibility (AD), the efficiency of conversion of digested food (ECD) and the efficiency of conversion of ingested food (ECI). Our results show that all traits tested presented a high percentage of nonadditive effects that modulate phenotype expression. We found a significant impact of family × diet interaction on pupal mass, RGR and ECD. Furthermore, these traits exhibited the greatest heritability. There was no evidence of presence of maternal effects. The results revealed that food utilization traits may evolve through epigenetics effects, such as phenotypic plasticity. This information can be used by modellers to improve forecast of spruce budworm population dynamics.
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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".