Anthropogenically-induced population increases in sympatric breeding arctic geese incur apparent competition consequences
Bibliographic record
Abstract
Populations of Lesser Snow Geese Anser caerulescens caerulescens and Cackling Geese Branta hutchinsii have increased substantially since the mid-20th century because of their utilisation of agricultural crops as a food resource on their wintering grounds. In contrast, Atlantic Brant Geese Branta bernicla hrota, which specialise in feeding on submerged marine vegetation in winter, have not capitalised on the availability of agricultural crops to the same extent. On the breeding grounds, Atlantic Brant are also adversely affected by multiple forms of competition from other sympatrically-nesting goose species. There has however been little research on apparent competition between these three species. Apparent competition occurs whenever the presence of one species leads to a reduced population density of another species when they share a generalist predator. To determine whether apparent competition stemming from higher goose nest densities might be a functional mechanism in limiting Atlantic Brant Goose reproduction, we monitored artificial nest survival in high, medium, and low goose nest densities at East Bay, Southampton Island, Nunavut, Canada in July 2015. Eggs in artificial nests located in high-density plots had the lowest survival probabilities compared to those in medium- and low-density plots. These results support the hypothesis that the increase in nest densities resulting from anthropogenically-induced population increases of sympatric native competitors could induce decreased breeding success and a cumulative decline in numbers of nesting Atlantic Brant via apparent competition.
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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 | 0.000 | 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".