Long‐Term Patterns of Immune Investment by Wild Deer Mice Infected with Sin Nombre Virus
Bibliographic record
Abstract
Immunocompetence of animals fluctuates seasonally, However, there is little consensus on the cause of these fluctuations. Some studies have suggested that these patterns are influenced by changes in reproductive condition, whereas others have suggested that differences result from seasonal variations in energy expenditures. The objective of our study was to examine these contrasting views of immunity by evaluating seasonal patterns of immune response and reproduction in wild populations of deer mice Peromyscus maniculatus exposed to Sin Nombre virus (SNV). Over three consecutive fall (September, October, November) and three consecutive spring (March, April, May) sampling periods, we used titration enzyme-linked immunosorbent assay (ELISA) to quantify virus-specific antibody production in 48 deer mice infected with SNV. Levels of reproductive hormones were quantified using ELISA. SNV antibody titers reached their lowest level during November (geometric mean titer [GMT] = 420) and their highest levels during September (GMT = 5,545) and May (GMT = 3,582), suggesting that the immune response of deer mice to SNV has seasonal patterns. The seeming decrease in antibody titer over winter coupled with the consistency in body masses suggests that during winter, immunocompetence may be compromised to offset the energetic costs of maintenance functions, including those associated with maintaining body mass. Deer mice showed distinct sex-based differences in SNV antibody production, with males producing higher antibody titers (GMT = 3,333) than females (GMT = 1,477). Levels of reproductive hormones do not appear to influence antibody production in either males or females, as there was no correlation between estradiol concentrations and SNV antibody titer in female deer mice (r² = 0.26), nor was there a significant relationship between levels of testosterone and SNV antibody titers in males (r² = 0.28). Collectively, this study demonstrates that immunocompetence of wild deer mice is seasonally variable; however, reproduction is not the primary stressor responsible for this variation. Rather, the data suggest that deer mice may compromise immunocompetence during winter to offset other maintenance costs during this period.
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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) | 0.000 | 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 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".