Associations between faecal chemical pollutants and hormones in primates inhabiting Kibale National Park, Uganda
Bibliographic record
Abstract
While anthropogenic pollutants are known to be a threat to primates, our understanding of exposure to pollutants in situ and their sub-lethal effects is still limited. We used non-invasive biomonitoring to examine associations between faecal concentrations of 97 chemical pollutants and faecal hormone metabolites of cortisol and oestradiol in four primate species inhabiting Kibale National Park, Uganda (chimpanzees— Pan troglodytes , olive baboons— Papio anubis , red colobus— Piliocolobus tephrosceles and red-tailed monkeys— Cercopithecus ascanius ). Across all species ( n = 71 samples), results demonstrated positive associations of organochlorine pesticides (OCPs) ( β = 0.143, p = 0.020) and organophosphate esters ( β = 0.112, p = 0.003) with cortisol in adult females. Additionally, we observed positive associations of OCPs ( β = 0.192, p = 0.013) and brominated flame retardants ( β = 0.176, p = 0.004) with cortisol in juveniles. Results suggest that cumulative pesticides and flame retardants are disruptive to endocrine function in these populations, which could have implications for development, metabolism and reproduction. Our study further demonstrates that faeces can be an important, non-invasive matrix for examining pollutant–hormone associations in wild primates and other critical wildlife populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".