Irruptive movements and breeding dispersal of snowy owls: a specialized predator exploiting a pulsed resource
Bibliographic record
Abstract
Mobility and irruptive movements have been proposed as mechanisms that could allow some diet specialists to inhabit and breed in environments with highly unpredictable resources, like the arctic tundra. The snowy owl, one of the main avian predators of the tundra, is known to specialize on lemmings during the breeding season. These small mammals are also well known for their tremendous spatial and temporal variations in abundance. We examined the spring (pre‐breeding, from March to June) movements of snowy owls by tracking 9 breeding females in the Canadian Arctic for up to 3 yr with satellite transmitters. We used state‐space modeling to assess searching behavior and measure breeding dispersal distances. We also ascertain lemming abundance at some of the sites used by the marked owls. Tracked owls displayed searching movements for extended periods (up to 108 d) and traveled over large distances (up to 4093 km) each spring. The distance between furthest apart searching areas in a given year averaged 828 km (range 220 to 2433 km). Settlement date, distance between searching areas, traveled distance and the duration of prospecting movements were longer in the year where density of lemmings recorded in the eastern High‐Arctic (Bylot Island) was lowest. Nonetheless, snowy owls settled in areas where local lemming abundance was relatively high. Individual breeding dispersal distance between consecutive years averaged 725 km (range 18 to 2224). Overall, the high mobility of female snowy owls allowed these diet specialists to behave as irruptive migrants and to sustain their reproductive activities during consecutive years even under highly fluctuating resources.
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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 | 0.000 |
| 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 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".