Evolution of hosts paying manifold costs of defence
Bibliographic record
Abstract
Hosts are expected to incur several physiological costs in defending against parasites. These include constitutive energetic (or other resource) costs of a defence system, facultative resource costs of deploying defences when parasites strike, and immunopathological costs of collateral damage. Here, we investigate the evolution of host recovery rates, varying the source and magnitude of immune costs. In line with previous work, we find that hosts paying facultative resource costs evolve faster recovery rates than hosts paying constitutive costs. However, recovery rate is more sensitive to changes in facultative costs, potentially explaining why constitutive costs are hard to detect empirically. Moreover, we find that immunopathology costs which increase with recovery rate can erode the benefits of defence, promoting chronicity of infection. Immunopathology can also lead to hosts evolving low recovery rate in response to virulent parasites. Furthermore, when immunopathology reduces fecundity as recovery rate increases (e.g. as for T-cell responses to urogenital chlamydiosis), then recovery and reproductive rates do not covary as predicted in eco-immunology. These results suggest that immunopathological and resource costs have qualitatively different effects on host evolution and that embracing the complexity of immune costs may be essential for explaining variability in immune defence in nature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".