Variation in Foraging Behavior and Body Mass in Broods of Emperor Geese (Chen Canagica): Evidence for Interspecific Density Dependence
Bibliographic record
Abstract
Abstract Broods of geese spend time feeding according to availability and quality of food plants, subject to inherent foraging and digestive constraints. We studied behavioral patterns of broods of Emperor Geese (Chen canagica) on the Yukon–Kuskokwim Delta, Alaska, and examined how feeding and alert behavior varied in relation to habitat and goose density. During 1994–1996, time spent feeding by Emperor Goose goslings and adult females was positively related to multispecies goose densities near observation blinds, and not to just Emperor Goose density. Similarly, body mass of Emperor Goose goslings was more strongly related (negatively) to multispecies goose densities than intraspecific densities. A grazing experiment in 1995 indicated that most above ground primary production by Carex subspathacea, a preferred food plant, was consumed by grazing geese. Those results demonstrate that interspecific competition for food occurred, with greatest support for goslings whose behavioral repertoire is limited primarily to feeding, digesting, and resting. Although the more abundant Cackling Canada Geese (Branta canadensis minima) differed from Emperor Geese in their preferred use of habitats during brooding rearing (Schmutz 2001), the two species occurred in equal abundance in habitats preferred by Emperor Goose broods. Thus, Cackling Canada Geese were a numerically significant competitor with Emperor Geese. Comparing these results to an earlier study, time spent feeding by goslings, adult females, and adult males were greater during 1993–1996 than during 1985–1986. During the interval between those studies, densities of Cackling Canada Geese increased two to three times whereas Emperor Goose numbers remained approximately stable, which implies that interspecific competition affected foraging behavior over a long time period. These density-dependent changes in foraging behavior and body mass indicate that interspecific competition affects nutrient acquisition and gosling growth, which has a demonstrated effect (Schmutz 1993) on juvenile survival of Emperor Geese. Management of Emperor Geese should consider interspecific relations and densities of all goose species occurring on the Yukon–Kuskokwim Delta, Alaska.
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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 | 0.001 |
| 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".