Density and abundance estimation of unmarked ungulates using camera traps in the Mudumu National Park, Namibia
Bibliographic record
Abstract
Abstract Density and abundance estimates are critical to effective wildlife management and are essential for monitoring population trends and setting effective quotas for harvesting. Management of roan (Hippotragus equinus) and sable (H. niger) antelopes in Mudumu National Park (MNP), Namibia, is challenging because they are elusive, naturally unmarked, and believed to occur at low densities. The species are threatened by habitat fragmentation, human population growth, and illegal hunting, and reliable density and abundance estimates have not been quantified, hampering management and conservation plans. Our objective was to estimate roan and sable densities and abundances using the time in front of the camera model (TIFC) and the Poisson-binomial N-mixture model (PB), respectively. We also evaluated the effects of environmental and ecological variables on roan and sable abundance. We used data from two camera trap surveys conducted between March and September 2021 in the MNP. Results showed that the TIFC model provided low-density estimates of 1.62 (95% CI 1.61–1.64) roans/km2and 2.46 (95% CI 2.42–2.50) sables/km2, consistent with trends reported in Africa where these species occur at low densities. In addition, the total abundance of roans and sables in the MNP from the PB model were 57 and 242, respectively. Higher roan abundance occurred in sites with higher grass cover. This study provides the first accurate camera trap-derived density and abundance estimates for roan and sable in the MNP, which will be critical for developing comprehensive conservation programs and strategies that are likely to reduce the risk of extinction for both species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".