Cumulative effects of widespread landscape change alter predator–prey dynamics
Bibliographic record
Abstract
Predator search efficiency can be enhanced by anthropogenic landscape change, leading to increased predator-prey encounters and subsequent prey population declines. Logging increases early successional vegetation, providing ungulate forage. This increased forage, however, is accompanied by linear feature networks that increase predator hunting efficiency by facilitating predator movement and increasing prey vulnerability. We used integrated step selection analyses to weigh support for multiple hypotheses representing the combined impact of logging features (cutblocks and linear features) on wolf (Canis lupus) movement and habitat selection in interior British Columbia. Further, we examine the relationship between logging and wolf kill-sites of moose (Alces alces) identified using spatiotemporal wolf location cluster analysis. Wolves selected for linear features, which increased their movement rates. New (0-8 years since harvest) cutblocks were selected by wolves. Moose kill-sites had a higher probability of occurring in areas with higher proportions of new and regenerating (9-24 years since harvest) cutblocks. The combined selection and movement responses by wolves to logging features, coupled with increased moose mortality sites associated with cutblocks, indicate that landscape change increases risk for moose. Cumulative effects of landscape change contribute to moose population declines, stressing the importance of cohesive management and restoration of anthropogenic features.
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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 | 0.001 | 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".