Quantifying the spatial distribution and trends of supplementary feeding sites in South Africa and their potential contribution to vulture energetic requirements
Bibliographic record
Abstract
Abstract Old world vultures are the most threatened group of raptors globally. Supplementary feeding sites (SFS) are a popular conservation tool, widely used to assist vulture populations. Despite their popularity, the impact of SFS on vultures remains largely unstudied. A lack of knowledge on the number, distribution and management of SFS is a key factor hindering such research. In this study, we compile records of SFS in South Africa and conduct questionnaires with SFS managers to characterize SFS. We identify 143 currently active SFS. Our data suggest that SFS numbers have been stable over the last decade. The average provisioning rate for all SFS was 64.6 kg day −1 . Overall SFS provide an estimated 3301 tonnes of food to scavengers each year, the equivalent of 83% of the energetic needs of all vultures in the region. This contribution was highly skewed, however, with just 17% of active SFS sites providing 69% of all food. Furthermore, these resources were not equally distributed, with SFS in Limpopo, North West and Kwazulu‐Natal provinces providing 83% of the total meat provisioned. The three most common meat types provided at SFS were beef (39%), pork (33%) and game (19%). Worryingly, we found that 68% and 28% of SFS managers were unaware of the potential harmful effects of lead and veterinary drugs, respectively, which highlights potential poisoning risks associated with SFS. Examining exposure to SFS by different vulture species, we found that whilst SFS are accessible across the distribution range of vultures with large home ranges (e.g. African white‐backed and Cape vultures), those species with smaller home ranges have relatively poor accessibility. With this study, we demonstrate the potential importance, but also associated risks, of SFS to vultures in South Africa, and provide the information base to assess the impacts of this popular but as yet largely unassessed conservation tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".