Trade‐offs between transgenerational transfer of nutritional stress tolerance and immune priming
Bibliographic record
Abstract
Summary Transgenerational effects are often assumed to have adaptive value as a driver of variation in offspring and parental fitness. Studies of transgenerational effects often focus on single environmental variables. However, in nature, it is unlikely for one factor to vary independently from others and there are likely to be trade‐offs between different stressors. We altered the nutritional environment of both parents and offspring to examine how nutritional stress influences transgenerational immune priming (TGIP) with a sublethal challenge by the bacterial pathogen Bacillus thuringiensis, in the cabbage looper, Trichoplusia ni. Transgenerational immune priming was adaptive when offspring encountered the same pathogen experienced by their parents, such that offspring resistance to B. thuringiensis increased 1·5‐fold and antibacterial activity in the haemolymph increased by as much as 4‐fold. However, this was not a general increase in pathogen resistance as susceptibility to a second pathogen, the baculovirus, TnSNPV remained the same. Interestingly, nutritional stress in the parents both enhanced nutritional stress tolerance of offspring and heightened resistance to both B. thuringiensis and TnSNPV. Elevated pathogen resistance was linked to increased egg size. There was a significant trade‐off between TGIP and the transfer of nutritional stress tolerance when parents encountered both stressors simultaneously, such that parents transferred resistance to pathogens but not nutritional stress tolerance. These results highlight the trade‐offs that can modulate the occurrence and magnitude of transgenerational effects and illustrate the importance of assessing interactions between multiple environmental variables. At high population densities, disease risk increases and resour‐ces become depleted. Thus, our findings could have significant implications for 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 | 0.001 |
| 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".