Predicting potential distributions of large carnivores in Kenya: An occupancy study to guide conservation
Bibliographic record
Abstract
Aim: Species distribution maps are frequently the foundation upon which species-specific conservation strategies are developed, however, mapping species distribution is challenging, especially across large spatial extents. Our aim was to use a novel empirical approach to predict the national distribution for all six large carnivore species found in Kenya to guide conservation and management decisions by identifying knowledge and conservation gaps. Location: Kenya Methods: Data on carnivore presence and absence were collected through questionnaires and sightings-based surveys. These data were combined and analysed using single-season false-positive occupancy models, which account for imperfect detections and false positives. To inform conservation strategies, we used the occupancy outputs to make predictions for unsampled areas and create occupancy-based distribution maps, where ψ>0.50, to (1) quantify differences with IUCN Red List range maps, (2) quantify overlap with wildlife areas and (3) identify areas of high carnivore richness. Results: Large carnivore occupancy was associated with land conversion, habitat, and prey availability. Our results suggest that all six species are widely distributed across Kenya and reveal substantial differences in distribution maps compiled by the IUCN Red List. More specifically, our occupancy-based distribution maps predict a much larger distribution for African wild dog (5.09X), lion (4.77X), and leopard (1.46X), similar distribution for cheetah, and smaller distribution for spotted hyaena (0.84X) and striped hyaena (0.65X). For all large carnivores, the vast majority (~80%) of their predicted distribution falls outside wildlife areas and northern Kenya is predicted to have the highest large carnivore richness. Main conclusions: Our results are encouraging as large carnivores may be widely distributed across Kenya, in some cases potentially more so than previously acknowledged. However, much of this range lies outside wildlife areas and represents areas of concern both for conservation and human livelihoods illustrating the challenges of conserving large carnivores across their range.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".