Non-destructive methods to assess pesticide exposure in free-living bats
Bibliographic record
Abstract
Bat populations are dwindling worldwide due to anthropogenic activities like agriculture, however the role that pesticide exposure plays on these declines is unclear. To address these research gaps, we first need to develop reliable methods to detect and monitor exposure to environmental pollutants and its effects on free-living bats. The use of biomarkers is a sensitive and informative tool to study sublethal effects in wildlife, however it requires laboratory validation and integrative approaches to be applicable to free-living species. In this study, we propose a set of non-destructive biomarkers to evaluate pesticide exposure in free-ranging bats and validated their suitability with dose-exposure experiments in captivity. We selected three biomarkers that have been widely used in vertebrate ecotoxicology and that combined represent sensitive, specific, and ecologically relevant responses to pollutants: DNA damage, AChE activity, and leukocyte profiles. We used two insectivorous bat species as model species Eptesicus fuscus (laboratory) and Pteronotus mexicanus (field). We found that micronuclei frequency (genotoxicity) and AChE activity (exposure and neurotoxicity) were robust indicators of toxicant exposure. The validity of this set of endpoints was supported by their consistent performance in laboratory and field experiments as well as by the significant correlation among them. Leukocyte profile (systemic stress) results were not consistent between laboratory and field studies, suggesting further evaluation of its suitability is needed. Integrative approaches, like the one we used here, maximize the insights about toxicant effects by combining the information of single biomarkers into more meaningful inferences, which can be applied to environmental risk assessments in wildlife. Furthermore, the use of non-destructive, cost-effective biomarkers is imperative when assessing toxicant exposure and effects in vulnerable wildlife and it should be a priority in the field of wildlife toxicology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".