Diverse hosts, diverse immune systems: evolutionary variation in bat immunology
Bibliographic record
Abstract
The ability of multiple bat species to host zoonotic pathogens without showing disease has fostered growing interest in bat immunology, indicating ways immune systems may differ between bats and other vertebrates. However, interspecific variation in immunological diversity among bats has only begun to be recognized. The order Chiroptera accounts for over 20% of all mammal species and shows extreme diversity in a suite of correlated ecological traits, such that bats should not be expected to be immunologically homogenous. Here, we review the ecological and evolutionary diversity of chiropteran hosts and highlight case studies emphasizing the range of immune strategies thus far observed across bat species, including responses to SARS-CoV-2. Next, we synthesize and propose hypotheses to explain this immunological diversity, focused on pathogen exposure, biogeography, host energetics, and environmental stability. Lastly, we analyze immunology citations across bat species to motivate discussion of outstanding research priorities. Broad sampling is needed to remedy current biases, as only a fraction of bat species has been immunologically studied. Such work should integrate methodological advancements, in vitro and in vivo studies, and phylogenetic comparative methods to robustly test evolutionary hypotheses and understand the drivers and consequences of immunological diversity among bats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".