Patterns of Brood Dispersal and Habitat Use in Emperor Goose (Anser canagicus) Goslings: Behavioral and Ecological Insights
Bibliographic record
Abstract
Abstract Background Animals make decisions about habitat use that have lifelong implications for survival and fitness. For waterfowl, decisions made between hatching and fledging are especially important, as precocial young must not only grow and complete their molt but also prepare for their first fall migration. Methods To better understand how emperor goose broods navigate heterogenous habitats during the brood rearing period, we deployed GPS transmitters on females with broods to track gosling movements on the Yukon-Kuskokwim Delta, Alaska. From this GPS data, we determined distance traveled by broods from the nesting site, brood home range size, types of habitats used, and resource selection. These results were compared to previous work on emperor goslings from the late 1990s and early 2000s. Results We found that emperor goose broods stayed within 5km of their nesting site and maintained an average home range of ~ 10km 2 . Both grazing lawn and mudflat habitats were present within their home range but broods preferentially selected grazing lawns. Broods were observed to forage for algae on mudflat and Carex sedges on grazing lawns. Conclusions Our results reveal that grazing lawn remains the most important habitat for brood-rearing emperor geese on the Yukon-Kuskokwim Delta, consistent with past studies; however, broods also appear to regularly forage on green algae present on mudflats when these areas are available during low tide. This is the first-known study that has documented emperor goslings extensively using non-vegetated mudflat habitats, which provide forage while allowing adults to better observe incoming predators and competing species. We also found that females and their broods travel short distances to reach brood-rearing sites and that home range size varied based on individual, not habitat used. Overall, their dependence on grazing lawns, combined with long-term declines in these habitats across the Yukon-Kuskokwim Delta, may have lasting implications on fitness of emperor goose broods.
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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 | 0.001 | 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".