Nowhere to hide: Effects of linear features on predator–prey dynamics in a large mammal system
Bibliographic record
Abstract
Rapid landscape alteration associated with human activity is currently challenging the evolved dynamical stability of many predator-prey systems by forcing species to behaviourally respond to novel environmental stimuli. In many forested systems, linear features (LFs) such as roads, pipelines and resource exploration lines (i.e. seismic lines) are a ubiquitous form of landscape alteration that have been implicated in altering predator-prey dynamics. One hypothesized effect is that LFs facilitate predator movement into and within prey refugia, thereby increasing predator-prey spatial overlap. We evaluated this hypothesis in a large mammal system, focusing on the interactions between boreal woodland caribou (Rangifer tarandus caribou) and their two main predators, wolves (Canis lupus) and black bears (Ursus americanus), during the calving season of caribou. In this system, LFs extend into and occur within peatlands (i.e. bogs and nutrient-poor fens), a habitat type highly used by caribou due to its refugia effects. Using resource selection analyses, we found that LFs increased predator selection of peatlands. Female caribou appeared to respond by avoiding LFs and areas with high LF density. However, in our study area, most caribou cannot completely avoid exposure to LFs and variation in female response had demographic effects. In particular, increasing proportional use of LFs by females negatively impacted survival of their neonate calves. Collectively, these results demonstrate how LFs can reduce the efficacy of prey refugia. Mitigating such effects will require limiting or restoring LFs within prey refugia, although the effectiveness of mitigation efforts will depend upon spatial scale, which in turn will be influenced by the life-history traits of predator and prey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".