Behaviourally-mediated interactions of landscape pattern shape predator-prey dynamics in highly altered landscapes
Bibliographic record
Abstract
Landscape structure plays a fundamental role in shaping predator-prey dynamics, often being a key determinant of predator-prey coexistence. Rapid alteration of landscape structure, however, can lead to changes in predator-prey interactions with the magnitude of such changes dependent upon the scale and intensity of alteration and animal behavioural responses to novel environmental stimuli. In the boreal forests of western Canada, linear features (e.g. roads, seismic lines and pipelines) from industrial activity are a ubiquitous form of landscape alteration and increasing evidence suggests their presence has impacted interactions between wolves ( Canis lupus ) and boreal caribou ( Rangifer tarandus caribou ), leading to caribou population declines. Using simulation analyses parameterized by empirical data, we demonstrate how linear features affect the ability of wolves to predate caribou and evaluate how the spatial configuration and density of linear features interacts with animal behaviour to influence caribou-wolf encounter rates. Model outputs yield insights into the spatial requirements of caribou for effectively reducing predation risk and further illustrate behavioural strategies that are theoretically optimal for caribou. We discuss how our spatially explicit modelling of predator-prey encounter rates can inform management actions aimed at minimizing anthropogenic impacts within caribou range as well as in other predator-prey systems.
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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 | 0.001 |
| 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".