Movement tactics of a mobile predator in a meta‐ecosystem with fluctuating resources: the arctic fox in the High Arctic
Bibliographic record
Abstract
Animal movement is a fundamental process shaping ecosystems at multiple levels, from the fate of individuals to global patterns of biodiversity. The spatio‐temporal dynamic of food resources is a major driver of animal movement and generates patterns ranging from range residency to migration and nomadism. Arctic tundra predators face a strongly fluctuating environment marked by cyclic microtine populations, high seasonality, and the potential availability of sea ice, which gives access to marine resources in winter. This type of relatively poor and highly variable environment can promote long‐distance movements and resource tracking in mobile species. Here, we investigated the winter movements of the arctic fox, a major tundra predator often described as a seasonal migrant or nomad. We used six years of Argos satellite telemetry data collected on 66 adults from Bylot Island (Nunavut, Canada) tracked during the sea ice period. We hypothesized that long‐distance movements would be influenced by spatio‐temporal changes in resource availability and individual characteristics. Despite strong annual and seasonal changes in resource abundance and distribution, we found that a majority of individuals remained resident, especially those located in an area characterized by highly predictable pulse resources (goose nesting colony) and abundant cached food items (eggs). Foxes compensated terrestrial food shortage by commuting to the sea ice rather than using long‐distance tracking or moving completely onto the sea ice for winter. Individual characteristics also influenced movement patterns: age positively influenced the propensity to engage in nomadism, suggesting older foxes may be driven out of their territories. Our results show how these mammalian predators can adjust their movement patterns to favor range residency despite strong spatio‐temporal fluctuations in food resources. Understanding the movement responses of predators to prey dynamics helps identifying the scales at which they work, which is a critical aspect of the functioning and connectivity among meta‐ecosystems.
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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".