Wintering space use and site fidelity in a nomadic species, the snowy owl
Bibliographic record
Abstract
Migratory species can exploit many habitats over vast geographic areas and adopt various patterns of space and habitat use throughout their annual cycle. In nomadic species, determinants of habitat use during the non‐breeding season are poorly known due to the unpredictability of their movement patterns. Here, we analysed variability in wintering space and habitat use by a highly nomadic species, the snowy owl, in eastern North America. Using 21 females tracked by satellite telemetry between 2007 and 2016, we 1) assessed how space use patterns in winter varied according to the type of environment (marine vs terrestrial), latitudinal zone (Arctic vs temperate), local snow conditions and lemming densities and 2) investigated winter habitat and site fidelity. Our results confirmed a high inter‐individual variation in patterns of habitat use by wintering snowy owls. Highly‐used areas were concentrated in the Arctic and in the marine and coastal environments. Owls wintering in the marine environment travelled over longer distances during the winter, had larger home ranges and these were divided in more smaller zones than individuals in terrestrial environments. Wintering home range sizes decreased with high winter lemming densities, use of the marine environment increased following high summer lemming densities, and a thick snow cover in autumn led to later settlement on the wintering ground. Contrary to expectations, snowy owls tended to make greater use of the marine environment when snow cover was thin. Snowy owls were highly consistent in their use of a given wintering environment and a specific latitudinal zone between years, but demonstrated flexibility in their space use and a modest site fidelity. The snowy owls’ consistency in wintering habitat use may provide them with advantages in terms of experience but their mobility and flexibility may help them to cope with changing environmental conditions at fine spatial scale.
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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 | 0.000 | 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 | 0.001 |
| 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".