Animal movement affects interpretation of occupancy models from camera‐trap surveys of unmarked animals
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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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".