Wetland hydrologic dynamics and duck productivity are declining in the Prairie Pothole Region, and they are linked
Bibliographic record
Abstract
The Prairie Pothole Region (PPR) of North America is a globally important area hosting >50 % of North America’s breeding ducks. Ducks in the PPR depend on wetlands and grasslands which have experienced accelerated losses in extent and quality due to agriculture. While other bird populations have declined, duck abundance reached record highs recently (2013–2017). We explored this discontinuity by examining monitoring data for trends in pond numbers (wetlands with ponded water) and interannual dynamics (water-level dynamics indexed by interannual change in pond numbers) and how those factors influenced duck productivity in the PPR during 1976–2019. Over time, pond numbers increased but their interannual dynamics declined, indicating stabilization of an ecosystem evolved with a dynamic climate. Our models accounted for 67 and 71 % of variation in productivity of PPR-obligate gadwall ( Mareca strepera ) and redheads ( Aythya americana ), respectively. Breeding productivity of these sentinel species was positively correlated with pond abundance and dynamics, and systematically declined. Accordingly, our analyses revealed sensitivity of breeding ducks to systematic change in the PPR previously obscured by increasingly abundant pond numbers. Interannual pond dynamics improved duck productivity and pond dynamics have declined indicating a de facto 44-year decline in duck productivity which is likely driven by water-level stabilization decreasing quality of brood-rearing wetlands. Residual temporal effects indicated that productivity has also declined for other reasons, such as agricultural land use changes. While mechanisms behind these correlations are speculative, they demonstrate the importance of further understanding land use and climate changes in the PPR for conservation of these important species and ecosystems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".