Habitat use and behaviour of Greater Snow Geese during movements from nesting to brood-rearing areas
Bibliographic record
Abstract
Many precocial birds make long-distance movements with their young after hatch to reach the best foraging sites. On Bylot Island, Nunavut, a large number of Greater Snow Goose ( Chen caerulescens atlantica L., 1758) families move 30 km from the main nesting colony (MNC) to reach the main brood-rearing area (MBR) soon after hatch. Geese moving from the MNC to the MBR generally rear lighter and smaller goslings than geese that avoid this movement by both nesting and rearing their brood at the MBR. In this study, we tested the hypotheses that use of low-quality habitats and an increase in the time spent walking at the expense of foraging during movements could explain the reduced growth of goslings in those families. We conducted visual observations to compare habitat use and selection as well as behaviour of geese during brood movements from the MNC to the MBR (i.e., at a transit area) with those of families that had already settled at the MBR. We also conducted aerial tracking to monitor habitat use of 16 radio-marked females during and after brood movements. Streams, wet polygons, and lakes, considered high-quality habitats in terms of feeding opportunities and predator refuges, were preferred, while upland, a low-quality habitat, was avoided at both the transit area and the MBR. However, broods were found in the upland habitat more often during movements than once settled on a rearing site. The behaviour of unmarked geese at the transit site did not differ from that of geese at the MBR. We suggest that reduced food intake in low-quality habitats during movements, but not the increase in time spent walking, may explain the reduction in growth observed at fledging in goslings moving from the MNC to the MBR.
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 | 0.000 |
| 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".