Population vulnerability to climate change linked to timing of breeding in boreal ducks
Bibliographic record
Abstract
Abstract Identifying and understanding why traits make species vulnerable to changing climatic conditions remain central problems in evolutionary and applied ecology. We used spring snow cover duration as a proxy for phenological timing of wetland ecosystems, and examined how snow cover duration during spring and during the entire snow season affected population dynamics of duck species breeding in the western boreal forest of North America, 1973–2007. We predicted that population level responses would differ among duck species, such that late‐nesting species with reduced flexibility in their timing of breeding, i.e. scaup (Aythya spp.) and scoter (Melanitta spp.), would be more strongly affected by changing snow cover conditions relative to species better able to adjust timing of breeding to seasonal phenology, i.e. mallard (Anas platyrhynchos) and American wigeon (Anas americana). Population growth rates of scaup and scoter were positively linked to spring snow cover duration; after accounting for effects of density dependence, larger populations resulted after springs with long snow cover duration than after springs with short snow cover duration. In contrast, population growth rates of mallard and wigeon were either negatively or only weakly associated with snow cover duration. Duck population models were then incorporated with snow cover duration derived from climate model simulations under the A2 emission scenario, and these predictions suggested that late‐nesting duck species will experience the most severe population declines. Results are consistent with a hypothesis that the gradual climatic warming observed in the western boreal forest of North America has contributed to and may continue to exacerbate population declines of scaup and scoter.
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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 | 0.001 | 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".