Coloured ornamental traits could be effective and non-invasive indicators of pollution exposure for wildlife
Bibliographic record
Abstract
Growth in human populations causes habitat degradation for other species, which is usually gauged by physical changes to landscapes. Corresponding habitat degradation to air and water is also common, but its effects on individuals can be difficult to detect until they result in the decline or disappearance of populations. More proactive measures of pollution usually combine abiotic samples of soil, water or air with invasive sampling of expendable species, but this approach sometimes creates ethical dilemmas and has limited application for threatened species. Here, we describe the potential to measure the effects of pollution on many species of birds and fish by using ornamental traits that are expressed as coloured skin, feathers and scales. As products of sexual selection, these traits are sensitive to environmental conditions, thereby providing honest information about the condition of their bearers as ready-made biomarkers. We review the documented effects of several classes of pollutants, including pharmaceuticals, pesticides, industry-related compounds and metals, on two classes of colour pigments, namely melanins and carotenoids. We find that several pollutants impede the expression of both carotenoids and brown melanin, while enhancing traits coloured by black melanin. We also review some of the current limitations of using ornamental colour as an indicator of pollution exposure, suggest avenues for future research and speculate about how advances in robotics and remote imagery will soon make it possible to measure these traits remotely and in a non-invasive manner. Wider awareness of this potential by conservation managers could foster the development of suitable model species and comparative metrics and lay a foundation for pollution monitoring that is more generalizable and biologically relevant than existing standards.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".