Disentangling interactions among mercury, immunity and infection in a Neotropical bat community
Bibliographic record
Abstract
Abstract Contaminants such as mercury are pervasive and can have immunosuppressive effects on wildlife. Impaired immunity could be important for forecasting pathogen spillover, as many land‐use changes that generate mercury contamination also bring wildlife into close contact with humans and domestic animals. However, the interactions among contaminants, immunity and infection are difficult to study in natural systems, and empirical tests of possible directional relationships remain rare. We capitalized on extreme mercury variation in a diverse bat community in Belize to test association among contaminants, immunity and infection. By comparing a previous dataset of bats sampled in 2014 with new data from 2017, representing a period of rapid agricultural land conversion, we first confirmed bat species more reliant on aquatic prey had higher fur mercury. Bats in the agricultural habitat also had higher mercury in recent years. We then tested covariation between mercury and cellular immunity and determined if such relationships mediated associations between mercury and bacterial pathogens. As bat ecology can dictate exposure to mercury and pathogens, we also assessed species‐specific patterns in mercury–infection relationships. Across the bat community, individuals with higher mercury had fewer neutrophils but not lymphocytes, suggesting stronger associations with innate immunity. However, the odds of infection for haemoplasmas and Bartonella spp. were generally lowest in bats with high mercury, and relationships between mercury and immunity did not mediate infection patterns. Mercury also showed species‐ and clade‐specific relationships with infection, being associated with especially low odds for haemoplasmas in Pteronotus mesoamericanus and Dermanura phaeotis . For Bartonella spp., mercury was associated with particularly low odds of infection in the genus Pteronotus but high odds in the subfamily Stenodermatinae. Synthesis and application . Lower general infection risk in bats with high mercury despite weaker innate defense suggests contaminant‐driven loss of pathogen habitat (i.e. anemia) or vector mortality as possible causes. Greater attention to these potential pathways could help disentangle relationships among contaminants, immunity and infection in anthropogenic habitats and help forecast disease risks. Our results also suggest that contaminants may increase infection risk in some taxa but not others, emphasizing the importance of considering surveillance and management at different phylogenetic scales.
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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 | 0.001 |
| 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".