Data from: Habitat and seasonal drivers of leukocyte profiles within and across Neotropical bat species
Bibliographic record
Abstract
Land conversion is a dominant form of environmental change that can alter infection dynamics in wildlife by affecting host immune defense. Such effects may be compounded by seasonal variation in resources and reproduction and vary among members of a host community, yet the combined effects of habitat, season, and species identity on wildlife immunity remain poorly understood. We tested within- and across-species effects of land conversion and seasonality on immunity in Neotropical bats by quantifying hematological markers of physiological stress and inflammation. We sampled seven species across a large preserve and a smaller forest fragment in northern Belize during both the dry and wet seasons. Using phylogenetic generalized linear mixed models, we tested the overall effects of habitat and season and quantified per-species impacts. Total white blood cell counts and neutrophil-to-lymphocyte ratios showed no overall habitat or seasonal effects but displayed strong species-specific responses. In contrast, the systemic inflammation response index increased across all species in the dry season and smaller fragments, suggesting general poor health in unfavorable conditions. Species-specific effects did not align with dietary guilds, indicating roles for finer-scale ecological traits. Our findings highlight the complex, species-dependent effects of environmental change on wildlife's immune defense.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".