Data from: Predator-mediated negative effects of overabundant snow geese on arctic-nesting shorebirds
Bibliographic record
Abstract
Overabundant species can strongly impact ecosystem functioning through trophic cascades. The strong increase in several arctic geese populations, primarily due to changes in agricultural practices in temperate regions, can have severe direct impacts on tundra ecosystems through vegetation degradation. However, predator-mediated negative effects of goose overabundance on other tundra species can also be significant but are poorly understood. We tested the hypothesis that goose abundance negatively affects arctic-nesting shorebirds by increasing nest predation pressure. We used six years of data collected within and near a greater snow goose colony (Chen caerulescens atlantica) to evaluate the effect of geese on the spatial variation in (1) the occurrence of shorebird nest predators, (2) the nest predation risk (with artificial shorebird nests), and (3) the occurrence of nesting shorebirds. We found that the goose colony had a strong influence on the spatial distribution of nest predators and nesting shorebirds. Occurrence of predators decreased, while occurrence of nesting shorebirds increased with distance from the centroid of the colony. The strength of these effects was modulated by lemming density, the preferred prey for predators. Shorebird nest predation risk also decreased with distance from the colony. Overall, these results indicate that goose abundance negatively affects arctic-nesting shorebirds through shared predators. Therefore, we show that the current decline of some arctic shorebird populations may be in part mediated by a negative effect of an overabundant species.
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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 | 0.000 | 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) | 0.004 | 0.001 |
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".