Dynamic balancing of risks and rewards in a large herbivore: Further extending predator–prey concepts to road ecology
Bibliographic record
Abstract
Animal behaviour is shaped by the ability to identify risks and profitably balance the levels of risks encountered with the payoffs experienced. Anthropogenic disturbances like roads generate novel risks and opportunities that wildlife must accurately perceive and respond to. Basic concepts in predator-prey ecology are often used to understand responses of animals to roads (e.g. increased vigilance, selection for cover in their vicinity). However, prey often display complex behaviours such as modulating space use given varying risks and rewards, and it is unclear if such dynamic balancing is used by animals in the context of road crossings. We tested whether animals dynamically balance risks and rewards relative to roads using extensive field-based and GPS collar data from elk in Yoho National Park (British Columbia, Canada), where a major highway completely bisects their range during most of the year. We analysed elk behaviour by combining hidden Markov movement models with a step-selection function framework. Rewards were indexed by a dynamic map of available forage biomass, and risks were indexed by road crossings and traffic volumes. We found that elk generally selected intermediate and high forage biomass, and avoided crossing the road. Most of the time, elk modulated their behaviour given varying risks and rewards. When crossing the highway compared with not crossing, elk selected for greater forage biomass and this selection was stronger as the number of highway crossings increased. However, with traffic volume, elk only balanced foraging rewards when they crossed a single time during a travel sequence. Using a road ecology system, we empirically tested an important component of predator-prey ecology-the ability to dynamically modulate behaviour in response to varying levels of risks and rewards. Such a test articulates how decision-making processes that consider the spatiotemporal variation in risks and rewards allow animals to successfully and profitably navigate busy roads. Applying well-developed concepts in predator-prey theory helps understand how animals respond to anthropogenic disturbances and anticipate the adaptive capacity for individuals and populations to adjust to rapidly changing environments.
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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 | 0.001 | 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) | 0.001 | 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 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".