Dietary eicosapentaenoic acid and docosahexaenoic acid are linearly retained by common insect crop pests (cabbage looper and bertha armyworm) and alter insect biomass
Bibliographic record
Abstract
Abstract The long‐chain omega‐3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are prevalent in aquatic ecosystems and are not part of the natural diet of herbivorous, terrestrial insects, which generally consume alpha‐linolenic acid (ALA) and linoleic acid (LNA). However, recent advances in genetic engineering have lead to the development of terrestrial crops that express the novel traits of EPA and DHA production. In the present study, we examine the effects of dietary EPA and DHA on the growth, development and fatty acid content of two crop pest insects: bertha armyworm and cabbage looper. Five experimental diets were formulated to include increasing amounts of pure EPA and DHA (in relation to the total diet lipid level), according to the ratios (EPA + DHA relative to a vegetable oil containing ALA and LNA): 0 (control), 0.25 : 0.75 (lowest), 0.5 : 0.5 (low), 0.75 : 0.25 (medium) and 1 : 0 (high). Dietary EPA and DHA had significant effects on development time, mass and fatty acid content in both species. Dietary treatment (interactive with time) had a significant effect on individual mass of both insects, indicating that, over time, EPA and DHA impacted growth. However, insect mass, development and morphology results are not linearly related with increasing dietary EPA and DHA. Both species retained EPA and DHA in adult form, and the body content of EPA and DHA was significantly, positively correlated with EPA and DHA diet treatments in both the bertha armyworm ( r 2 = 91.3%) and cabbage looper ( r 2 = 75.8%). Dietary EPA and DHA could have fitness consequences for these organisms and could be nutritionally transferred to higher consumers.
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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) | 0.002 | 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".