Movement-integrated habitat selection reveals wolves balance ease of travel with human avoidance in a risk-reward trade-off
Bibliographic record
Abstract
Abstract Anthropogenic linear features are often linked to alterations in wildlife behaviour and movement. Landscape features such as habitat can have important mediating effects on wildlife response to disturbance and yet is rarely explicitly considered in how it interacts with other features. This study tests the effects of habitat variation on the space-use responses of GPS-collared wolves to linear features in eastern Manitoba. We simultaneously model wolf movement and selection within a conditional logistic regression framework (integrated Step Selection Analysis) with explicit consideration for how habitat alters these responses through either added cover or friction to movement. Classifying linear features based on the selection and movement response of wolves revealed that pairing transmission lines and primary roads increased the avoidance response to be greater than either feature on their own and provided evidence of a semi-permeable barrier to movement. In contrast, features with reduced human activity, including secondary and tertiary roads were highly selected for and provided access to movement corridors. Explicit parameterization of habitat provides evidence that where a linear feature is routed and which habitats it interacts with will have the greatest implications for wolf behavioural responses. Reduction of avoidance behavioural in highly risky environments signifies the importance of habitat for maintaining landscape connectivity, particularly when routing multiple features together. Natural regrowth along these features reduced movement advantages by increasing friction, indicating that actively decommissioning other features such as secondary roads would have important implications for reducing wolf encounters with prey. Knowing the influence of adjacent habitats on the likelihood of wolves selecting for a given linear feature creates context to minimize the impact of new anthropogenic features on behaviour.
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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 | 0.000 | 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) | 0.002 | 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".