Temporal and multi-spatial environmental drivers of duck nest survival
Bibliographic record
Abstract
Nest survival is determined in part by a combination of large-scale environmental factors and local nest-site characteristics. Because predation is the primary cause of nest failure, those drivers likely operate by influencing predator abundance, behavior, and/or nest detectability. For example, fluctuations in landscape productivity have the potential to alter predator and prey abundance, whereas nest vegetation and patterns of nest spacing may influence predator behavior. We used 8 yr of site-specific environmental data coupled with data collected from 11,547 duck nests to evaluate the relative importance of large-scale and local factors on nest survival. We found that higher values of gross primary productivity, basins, and pond counts were associated with higher nest survival in a given year, but were associated with lower nest survival the following 2 yr. Taken in combination with the literature, our interpretation is that productive environmental conditions can result in time-lagged increases in predator abundance, leading to higher levels of nest predation in subsequent years. Local factors were generally less important than large-scale covariates in determining duck nest survival, but we found that nests laid earlier, in thicker vegetation, and with closer nearest neighbors had higher survival rates. However, as the season progressed, nests with closer nearest neighbors had lower survival rates (significant initiation date*distance interaction), suggesting predators may eventually aggregate in areas of higher nest density. Our results highlight the importance of both large-scale and local factors as they affect duck nest survival, and suggest several hypotheses about predator numerical and aggregative responses that are ripe for empirical testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".