Anthropogenic Impact and Antimicrobial Resistance Occurrence in South American Wild Animals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a significant global challenge that affects environmental, animal, and human health, with reports of antimicrobial-resistant bacteria and antimicrobial resistance genes becoming increasingly common across these domains. This study aimed to systematically review and compare the occurrence of AMR in bacterial isolates from wild animals in South America, focusing on environments with varying levels of anthropogenic impact. Half of the countries in South America documented AMR in wild animals at least once. Most studies focused on specific animal classes, particularly Aves and Mammalia, with a notable emphasis on the orders Chiroptera and Rodentia, as well as the bacterial species Escherichia coli and Salmonella enterica. Subgroup meta-analyses revealed that, for most antimicrobials, the proportion of AMR was significantly higher in environments with a high anthropogenic impact compared to those with a low anthropogenic impact. However, there were no significant differences between the two types of environments for some antimicrobials. Interestingly, certain beta-lactams showed a higher proportion of AMR in environments with low anthropogenic impact. These findings raise important questions regarding the origins and spread of AMR in wild animals, underscoring the necessity for further research to understand the dynamics of AMR in areas with varying levels of human intervention.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".