Behavioral “bycatch” from camera trap surveys yields insights on prey responses to human‐mediated predation risk
Bibliographic record
Abstract
Abstract Human disturbance directly affects animal populations and communities, but indirect effects of disturbance on species behaviors are less well understood. For instance, disturbance may alter predator activity and cause knock‐on effects to predator‐sensitive foraging in prey. Camera traps provide an emerging opportunity to investigate such disturbance‐mediated impacts to animal behaviors across multiple scales. We used camera trap data to test predictions about predator‐sensitive behavior in three ungulate species (caribou Rangifer tarandus; white‐tailed deer, Odocoileus virginianus; moose, Alces alces) across two western boreal forest landscapes varying in disturbance. We quantified behavior as the number of camera trap photos per detection event and tested its relationship to inferred human‐mediated predation risk between a landscape with greater industrial disturbance and predator activity and a “control” landscape with lower human and predator activity. We also assessed the finer‐scale influence on behavior of variation in predation risk (relative to habitat variation) across camera sites within the more disturbed landscape. We predicted that animals in areas with greater predation risk (e.g., more wolf activity, less cover) would travel faster past cameras and generate fewer photos per detection event, while animals in areas with less predation risk would linger (rest, forage, investigate), generating more photos per event. Our predictions were supported at the landscape‐level, as caribou and moose had more photos per event in the control landscape where disturbance‐mediated predation risk was lower. At a finer‐scale within the disturbed landscape, no prey species showed a significant behavioral response to wolf activity, but the number of photos per event decreased for white‐tailed deer with increasing line of sight (m) along seismic lines (i.e., decreasing visual cover), consistent with a predator‐sensitive response. The presence of juveniles was associated with shorter behavioral events for caribou and moose, suggesting greater predator sensitivity for females with calves. Only moose demonstrated a positive behavioral association (i.e., longer events) with vegetation productivity (16‐day NDVI), suggesting that for other species bottom‐up influences of forage availability were generally weaker than top‐down influences from predation risk. Behavioral insights can be gleaned from camera trap surveys and provide complementary information about animal responses to predation risk, and thus about the indirect impacts of human disturbances on predator–prey interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".