Landscape composition, climate variability, and their interaction drive waterfowl nest survival in the Canadian Prairies
Bibliographic record
Abstract
Abstract For upland‐nesting ducks that rely on grassland and wetland habitats in landscapes characterized by human alteration and a strongly fluctuating climate, information on how landscape composition interacts with climate is needed to better understand the security of conservation investments and to inform adaptive management in a changing climate. We characterized spatial and temporal variation in a 10‐year study of nest survival of five species of upland‐nesting ducks at study areas across the Canadian Prairie Pothole Region. We assessed factors affecting nest survival across gradients of landscape composition, climate, and duck density. Habitat characteristics influenced nest survival at multiple scales spanning nest‐scale vegetation density to landscape‐scale perennial cover abundance, with the relative magnitude of spatial variation in nest survival greater than that of temporal variation in this study. An interaction between climate and landscape composition suggested that intact landscapes can moderate the effects of interannual variation in climate, reinforcing the importance of habitat conservation in a changing climate. Measured nest survival rates ranged from 4.7% to 40.5% and were high enough to sustain duck populations in only 31% of study areas, barring sustained climatic conditions suitable for elevating other breeding season vital rates, suggesting the need for continued investment in waterfowl conservation.
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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 | 0.001 | 0.001 |
| 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".