Consequences of rainfall variation for breeding wetland blackbirds
Bibliographic record
Abstract
Annual variability in abiotic factors can be pronounced, especially in systems that rely on precipitation, such as arid regions and prairie potholes. We report how annual variation in precipitation from 1999 to 2002 in the Prairie Pothole Region of Iowa, USA, affected both density and reproduction of two interspecific competitors: yellow-headed blackbirds, Xanthocephalus xanthocephalus (Bonaparte, 1826), and red-winged blackbirds, Agelaius phoeniceus (L., 1766). During dry years, yellow-headed blackbirds, an obligate wetland-breeding species, showed a marked reduction in density and a complete reproductive failure in which none of the nests we monitored fledged young. The reproductive failure was attributed primarily to nest predation, which was negatively correlated with water levels in wetlands. Conversely, red-winged blackbirds, a facultative wetland-breeding species, showed little variation in density and nest success. Both species exhibited similar patterns of reduced clutch size and later nest initiation dates in dry years, measures often tied to bottom-up effects of food availability and (or) age of individuals. Yet top-down effects of nest predation had a stronger influence, because lower clutch size did not result in fewer young fledged per successful nest. Incorporating how rainfall variation can affect wetland songbird demography will be critical for understanding population and community dynamics in changing environments.
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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 | 0.000 | 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".