Characterizing wildlife behavioural responses to roads using integrated step selection analysis
Bibliographic record
Abstract
Summary Roads are a prevalent, ever‐increasing form of human disturbance on the landscape. In many places in western North America, energy development has brought human and road disturbance into seasonal winter range areas for migratory elk. We sought to evaluate the predictions from the risk‐disturbance hypothesis when studying elk response to roads during winter. Road proximity and crossing were used to evaluate these behaviours, which offered a rare comparison between two common measures of roads. We used integrated step selection analysis ( iSSA ) to evaluate four alternative hypotheses regarding the influence of roads on space‐use behaviour across 175 elk‐years of elk telemetry data, and we quantified both population‐level and individual‐level variations in responses. We demonstrated, for the first time, how iSSA can be used to combine movement analysis in a refined approach to habitat selection. Elk responded to roads as they would natural predation risk. Elk selected areas farther from roads at all times of day with avoidance being greatest during twilight. In addition, elk sought cover and moved more when in the vicinity of roads. Road crossings were generally avoided, but this avoidance was weakest during daytime when elk were both moving and closer to roads. Synthesis and applications . Energy development is transforming landscapes in western North America with the proliferation of roads, which we show is having substantial and multifaceted negative effects on elk movement and behaviour. These adverse effects can be mitigated by minimizing new road construction and by restricting traffic on roads as well as providing the protection of tree cover on elk winter ranges.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".