Effects of agricultural change on abundance, fitness components and distribution of two arctic‐nesting goose populations
Bibliographic record
Abstract
Abstract Intensification of agriculture since the 1950s has enhanced the availability, competitive ability, crude protein content, digestibility and extended growing seasons of forage grasses. Spilled cereal grain also provides a rich food source in autumn and in winter. Long‐distance migratory herbivorous geese have rapidly exploited these feeding opportunities and most species have shown expansions in range and population size in the last 50 years. Results of long‐term studies are presented from two Arctic‐breeding populations, the Svalbard pink‐footed goose and the Greenland white‐fronted goose (GWFG). GWFGs have shown major habitat shifts since the 1950s from winter use of plant storage organs in natural wetlands to feeding on intensively managed farmland. Declines in local density on, and abandonment of, unmodified traditional wintering habitat and increased reproductive success among those birds wintering on farmland suggest that density‐dependent processes were not the cause of the shift in this winter‐site‐faithful population. Based on enhanced nutrient and energy intake rates, we argue that observed shifts in both species from traditionally used natural habitats to intensively managed farmland on spring staging and wintering areas have not necessarily been the result of habitat destruction. Increased food intake rates and potential demographic benefits resulting from shifts to highly profitable foraging opportunities on increasingly intensively managed farmland, more likely explain increases in goose numbers in these populations. The geographically exploratory behaviour of subdominant individuals enables the discovery and exploitation of new winter feeding opportunities and hence range expansion. Recent destruction of traditional habitats and declines in farming at northern latitudes present fresh challenges to the well being of both populations. More urgently, Canada geese colonizing breeding and moulting habitats of white‐fronted geese in Greenland are further affecting their reproductive output.
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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".