Old World Vultures Reflect Effects of Environmental Pollutants Through Human Encroachment
Bibliographic record
Abstract
African wildlife face challenges from many stressors including current and emerging contaminants, habitat and resource loss, poaching, intentional and unintentional poisoning, and climate-related environmental change. The plight of African vultures exemplifies these challenges due to environmental contaminants and other stressors acting on individuals and populations that are already threatened or endangered. Many of these threats emanate from increasing human population size and settlement density, habitat loss from changing land use for agriculture, residential areas, and industry, and climate-related changes in resource availability. Environmental chemicals that are hazardous include legacy chemicals, emerging chemicals of concern, and high-volume-use chemicals that are employed as weed killers and in other agricultural applications. Furthermore, there are differences in risk for species living in close proximity to humans or in areas affected by habitat loss, climate, and industry. Monitoring programs are essential to track the status of nesting pairs, offspring survival, longevity, and lifetime productivity. This is important for long-lived birds, such as vultures, that may be especially vulnerable to chronic exposure to chemicals as obligate scavengers. Furthermore, their position in the food web may increase risk due to biomagnification of chemicals. We review the primary chemical hazards to Old World vultures and the interacting stressors affecting these and other birds. Habitat is a major consideration for vultures, with tree-nesters and cliff-nesters potentially experiencing different risks of exposure to environmental chemicals. The present review provides information from long-term monitoring programs and discusses a range of these threats and their effects on vulture populations. Environ Toxicol Chem 2022;41:1586-1603. © 2022 SETAC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".