Microbial surveillance versus cytokine responsiveness in native and non-native house sparrows
Bibliographic record
Abstract
The success of introduced species often relies on flexible traits, including immune system traits. While theories predict non-natives will have weak defences due to decreased parasite pressure, effective parasite surveillance remains crucial, as infection risk is rarely zero and the evolutionary novelty of infection is elevated in non-native areas. This study examines the relationship between parasite surveillance and cytokine responsiveness in native and non-native house sparrows, hypothesizing that non-natives maintain high pathogen surveillance while avoiding costly inflammation. We made this specific prediction, as this pattern could enable invaders to effectively mitigate pathogen risk in a manner commensurate with the life-history priorities of a colonizing organism (i.e. rapid maturation and high reproductive effort). To test this hypothesis, we measured TLR -2 and TLR -4 expression, markers of pathogen surveillance and cytokine responses (changes in IL-1β and IL-10 ), regulators of inflammation, to a simulated bacterial infection. In non-native sparrows, we found that as TLR -4 expression increased, IL-1β and IL-10 responses decreased, a relationship not observed in native sparrows. Additionally, higher body condition predicted larger IL-1β and IL-10 responses in all birds. These findings suggest that high TLR-4 surveillance may mitigate strong inflammatory responses in non-native sparrows, with pathological and resource-based costs driving immune variation among and within populations.
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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) | 0.001 | 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".