Using photo by-catch data to reliably estimate spotted hyaena densities over time
Bibliographic record
Abstract
Abstract Protected areas are becoming increasingly isolated refugia for large carnivores but remain critical for their survival. Spotted hyaenas (Crocuta crocuta) are important members of the African large carnivore guild but, like other members of the guild, routinely come into conflict with people because of their large home ranges that are not always confined to protected areas. To effectively conserve spotted hyaena populations, it is paramount to monitor their abundance through reliable and cost-effective techniques. We estimated the density of spotted hyaenas in Hluhluwe–iMfolozi Park (HiP), South Africa using camera trap images and a spatially explicit capture-recapture (SECR) framework between 2013 and 2018. We estimated an average of 18.29 ± 3.27 spotted hyaenas per 100 km2 between 2013 and 2018, with an annual estimated high of 20.83/100 km2 in 2014 and a low of 11.98/100 km2 in 2015. Our results demonstrate that camera trap by-catch data can be used for estimating spotted hyaena densities over time. We believe that given the widespread use and deployment of camera traps across Africa, collaborative efforts to use existing data to improve regional and continental estimates and population trends for spotted hyaenas should be a priority.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".