Predicting space use patterns of a territorial top predator: from individual movement decisions to Arctic fox space use
Bibliographic record
Abstract
Abstract Predicting animal space use could greatly improve our understanding and forecasting of ecological processes. Despite growing interest, the development of predictive space use models amenable to the integration of spatial processes into ecological frameworks have yet to reach their full potential. Using high-resolution tracking data collected at 4-minute intervals from 26 Arctic foxes over five years, we developed a predictive space use model based on a step-selection approach. We assessed fine-scale habitat selection in relation to prey distribution, landscape features, and ecological constraints such as central place foraging and territoriality. We then used these results to build an agent-based model simulating fox space use and evaluated its ability to reproduce observed space use patterns. Step-selection analyses confirmed that fox movements were driven by habitat type, goose nest density, distance to den, and avoidance of distance to the home range boundary. Agent-based simulations closely matched empirical tracking data and accurately forecasted fox space use, even for individuals excluded from model parameterization. By developing a predictive model of predator space-use, our study provides a foundation for incorporating additional components of the predation sequence and contributes to more spatially informed approaches in predator-prey ecology.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".