The predator activity landscape predicts the anti-predator behavior and distribution of prey in a tundra community
Bibliographic record
Abstract
Abstract Predation shapes communities through consumptive and non-consumptive effects, where in the latter prey respond to perceived predation risk through risk management strategies occurring at different spatial and temporal scales. The landscape of fear concept is useful to better understand how predation risk affects prey behavioral decisions and distribution, and more generally the spatial dimension of predator-prey relationships. We assessed the effects of the predation risk landscape in a terrestrial Arctic community, where arctic fox is the main predator of ground-nesting bird species. Using high frequency GPS data, we developed a predator activity landscape resulting from fox space use patterns, and validated with an artificial prey experiment that it generated a predation risk landscape. We then investigated the effects of the fox activity landscape on multiple prey, by assessing the anti-predator behavior of a primary prey (snow goose) and the nest distribution of several incidental prey. Areas highly used by foxes were associated with a stronger level of nest defense by snow geese. We further found a lower probability of occurrence of incidental prey nests in areas highly used by foxes, but only for species nesting in habitats easily accessible to foxes. Species nesting in refuges consisting of micro-habitats limiting fox accessibility, like islets, did not respond to the fox activity landscape. Consistent with the scale of the fox activity landscape, this result reflected the capacity of refuges to allow bird nesting without regard to predation risk in the surrounding area. We demonstrated the value of using predator space use patterns to infer spatial variation in predation risk and better understand its effects on prey in landscape of fear studies. We also exposed the diversity of prey risk management strategies, hence refining our understanding of the mechanisms driving species distribution and community structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".