Perceptions of vulture supplementary feeding site managers and potential hidden risks to avian scavengers
Bibliographic record
Abstract
Abstract Under the current African vulture crisis, supplementary feeding sites (SFS), which provide carrion resources, have become a popular conservation tool to address vulture declines. In South Africa, this practice is unregulated and the context in which SFS operate and their adherence to best management practices is currently unknown. In this study, we conducted a survey with SFS managers regarding the management of their SFS to evaluate potential conservation implications of different practices. Half of the SFS surveyed were associated with livestock farming. Overall, most managers (84%) perceived some benefit from running an SFS, largely attributed to cleaning services provided by vultures. Over half of the managers perceived no disadvantages from running SFS. We found a positive correlation between numbers of vultures seen at SFS and the amount of food provided there. Despite unintentional and intentional poisoning being identified by experts as the most critical threats to vultures in Southern Africa, only 47 and 24% of managers, respectively, listed these as potential threats to vultures, highlighting limited understanding of current vulture conservation issues. Most managers (85%) vetted carcasses for provisioning suitability based on whether they had been treated with veterinary drugs, but relatively few managers (10%) did the same for lead (Pb) contamination. Only 30% of managers considered threats to vultures when they decided on a location for their SFS. Overall, this study unveils that at many SFS, safety conditions are not met and vultures may be exposed to risks, such as the ingestion of toxic substances (e.g., Pb) or electrocution by energy infrastructure. To minimize unintended negative consequences from SFS, it will be essential to increase the interaction between SFS managers and conservation practitioners, to increase the flow of information on best management practices and enforce stringent and clear guidelines that minimize any risks to vultures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".