Environmental variability affects optimal trade‐offs in ecological immunology
Bibliographic record
Abstract
Abstract The costs of mounting and maintaining an immune response lead to trade‐offs with investment in (or maintenance of) other organ systems or functions such as reproduction, as has been observed in several taxa, including birds, insects, and mammals. Given these trade‐offs, optimal strategies can be affected by biotic and abiotic conditions; however, how these trade‐offs are affected by environmental variability remains unclear. Using a deterministic within‐host model of disease dynamics, we analyzed the effect of environmental variability (here, the coefficient of variation in the host’s background survival distribution) on the optimal resolution to a trade‐off between investment in reproduction and the immune system. We found that higher variability led to increased investment in the immune system at the cost of decreased early‐life reproduction when the immune investment strategy was assumed to be constant through life. Since we manipulated the distribution of lifespans while keeping the mean lifespan constant, our results suggest that even the small probability of living far past the mean lifespan for individuals in highly variable environments has a large effect on the optimal investment strategy. When immune investment was able to change over the age of the organism, the optimal investment strategies were again contingent on the level of environmental variability experienced by individuals. Our results support and extend previous work on optimal life‐history theory in variable environments by showing that in the context of ecological immunology, the most fit individuals in constant environments are not the most fit individuals in variable environments. The effects of environmental variability on optimal immune investment are likely to have consequences for other aspects of infectious disease such as transmission dynamics, virulence evolution, and the likelihood of epidemics occurring.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".