Genetic and phenotypically flexible components of seasonal variation in immune function
Bibliographic record
Abstract
Animals cope with seasonal variation in environmental factors by adjustments of physiology and life history. When seasonal variation is partly predictable, such adjustments can be based on a genetic component or be phenotypically flexible. Animals have to allocate limited resources over different demands, including immune function. Accordingly, immune traits could change seasonally, and such changes could have a genetic component that differs between environments. We tested this hypothesis in genotypically distinct groups of a widespread songbird, the stonechat (Saxicola torquata). We compared variation in immunity during 1 year in long-distance migrants, short-distance migrants, tropical residents and hybrids in a common garden environment. Additionally, we investigated phenotypically flexible responses to temperature by applying different temperature regimes to one group. We assessed constitutive immunity by measuring hemagglutination, hemolysis, haptoglobin and bactericidal ability against Escherichia coli and Staphylococcus aureus. Genotypic groups differed in patterns of variation of all measured immune indices except haptoglobin. Hybrids differed from, but were rarely intermediate to, parental subspecies. Temperature treatment only influenced patterns of hemolysis and bactericidal ability against E. coli. We conclude that seasonal variation in constitutive immunity has a genetic component, that heredity does not follow simple Mendelian rules, and that some immune measures are relatively rigid while others are more flexible. Furthermore, our results support the idea that seasonal variability in constitutive immunity is associated with variability in environment and annual-cycle demands. This study stresses the importance of considering seasonal variation in immune function in relation to the ecology and life history of the organism of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".