Africa's apex predator, the lion, is limited by interference and exploitative competition with humans
Bibliographic record
Abstract
Apex predators are crucial for maintaining ecological patterns and processes, yet humans hinder their ability to fulfil this role by displacing them from the landscape. Many apex predator species such as African lions ( Panthera leo ) are experiencing catastrophic declines as a result of competition with growing human populations. Increasing our understanding of the competitive interactions between lions and humans, as well as identifying thresholds of lion tolerance to human activities are important both for lion conservation and our understanding of apex predator ecology in the Anthropocene. We investigated the relative and cumulative influences of anthropogenic pressures on lion occurrence across a 73 000 km 2 multi-use landscape in southern Africa. We developed occupancy models from replicated detection/non-detection spoor surveys across gradients of anthropogenic and biotic features. We tested the two hypotheses that African lions were most limited by 1) interference competition with humans or 2) exploitative competition with humans and evaluated the relative contribution of individual anthropogenic and biotic variables to lion occurrence. Our models predicted that lions occupied 49% of the landscape. The strongest determinants of lion occupancy were negative associations with pastoralism and bushmeat poaching, and a positive association with preferred prey. Thus, lions in this landscape are limited by a combination of interference and exploitative competition with poachers and pastoralists. However, interference competition with pastoralism was the biggest driver limiting lion occupancy, with a clear disturbance threshold for lions cumulating in a near complete loss of lions from the landscape when cattle surpass 21% occurrence. This study provides a predictive understanding of the top-down impacts of humans on the world's vulnerable apex carnivores.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".