Camera traps placed to detect predators underestimate prey densities
Bibliographic record
Abstract
Abstract Mountain ungulates play an important role in ecosystems as primary consumers and as prey for rare predators. Monitoring their populations is therefore critical for conservation efforts. Within the 12 countries comprising the range of the snow leopard Panthera uncia , camera traps are routinely deployed to estimate numbers of this apex predator, providing an opportunity to also estimate numbers of their prey using bycatch data. However, the relative accuracy of the resulting prey density estimates compared to field surveys targeted specifically at prey species was unknown. We compared the performance of distance sampling based on camera-trap data with field surveys to estimate population densities of bharal Pseudois nayaur . We assessed estimates of bharal numbers from cameras placed to detect snow leopards (where ungulate captures presented bycatch data) against estimates from cameras placed specifically to detect bharal and then compared both with an independent estimate of bharal density from double-observer surveys and a total count of all bharal in the study area. The double-observer field surveys suggested a density of 1.94 bharal/km 2 , which was similar to the density derived from the total count (1.92 bharal/km 2 ). By comparison, we estimated density to be 2.11 bharal/km 2 from camera-based distance sampling and 0.35 bharal/km 2 from cameras placed to detect snow leopards (bycatch data). The density estimate from the ungulate bycatch data was significantly lower than that from the double-observer field survey and from the total count. It was also less precise, more costly and more time-consuming to obtain. Our results caution against using bycatch data from surveys designed for predators to estimate ungulate prey densities and indicate that tailored survey methods are required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".