Animal movement affects interpretation of occupancy models from camera‐trap surveys of unmarked animals
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Bibliographic record
Abstract
Abstract Occupancy models are increasingly applied to data from wildlife camera‐trap ( CT ) surveys to estimate distribution, habitat use, or relative abundance of unmarked animals. Fundamental to the occupancy modeling framework is the temporal pattern of detections at camera stations, which is influenced by animal population density and the speed and scale of animal movement. How these factors interact with CT sampling designs to affect the interpretation of occupancy parameter estimates is unclear. We developed a simple yet ecologically relevant animal movement simulation to create CT detections for animal populations varying in movement rate, home range area, and population density. We also varied CT sampling design by the duration of sampling and the density of CT s in our simulated domain. A single‐species occupancy model was fitted to simulated detection histories, and model‐estimated probabilities of occupancy were compared to the asymptotic proportion of area occupied ( PAO ), calculated as the union of all simulated home ranges. Occupancy model parameter estimates were sensitive to simulated movement and sampling scenarios. Occupancy models overestimated asymptotic PAO when a low population density of simulated animals moved quickly over large home ranges and this positive bias was insensitive to sampling duration. Conversely, asymptotic PAO was underestimated when simulated animals moved slowly in large‐ or intermediately sized home ranges. This negative bias decreased with increasing sampling duration and a lower density of CT s. Our results emphasize that the interpretation of occupancy models depends on the underlying processes driving CT detections, specifically animal movement and population density, and that model estimates may not reliably reflect variation in these processes. We recommend carefully defining occupancy if it is applied to CT data in order to better match sampling and analytical frameworks to the ecology of sampled wildlife species.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it