The extent and nature of the commercial captive lion industry in the Free State province, South Africa
Bibliographic record
Abstract
The captive breeding of wildlife for commercial purposes is a controversial issue. In South Africa, the farming of African lions ( Panthera leo ) for commercial trade emerged in the early 1990s, partly as a conservation measure to reduce the decline in wild lion numbers while meeting increasing wildlife trade demands. In May 2021, the South African Department of Forestry, Fisheries, and the Environment (DFFE) announced plans to end the captive breeding of lions, keeping of lions in captivity, and the use of captive lions, their parts and derivatives for commercial trade. Here, we examined the commercial captive lion industry from 2017 to 2020 in the Free State province, the heart of the lion breeding industry. We document the extent of the industry and highlight a number of key management issues. Of particular concern were issues with microchip numbers, which are used to follow each registered lion from birth to death through the system and to avoid laundering of wild caught and/or non-registered lions. Of the 4,823 unique microchips that were identified, at least 11% could not be followed through the system. Additionally, a minimum of 199 microchip numbers may have been reused by permit holders, either on captivity, euthanasia, or transport permits, indicating potential non-compliance with the Threatened or Protected Species (TOPS) Regulations. We highlight further areas of concern that warrant additional attention for these types of activities that may cause management issues during the transition period and which may also be relevant for the keeping, breeding, and trading of other TOPS regulated species in South Africa, particularly big cat species.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".