Is predation on waterfowl nests density dependent? – Tests at three spatial scales
Bibliographic record
Abstract
We tested whether predation on duck nests ( Anas spp.) was density dependent at three spatial scales using artificial and natural nests in the Suisun Marsh, California, USA. At the largest spatial scale, we used 5 years (1998–2002) of data to examine the natural variation in duck nest success and nest densities among 8–16 fields per year, each 5–33 ha in size (n=62 fields). At an intermediate spatial scale, we deployed artificial nests (2000, n=280) within 1‐ha plots at three experimental densities (5, 10, and 20 nests ha −1 ) in a complete randomized block design and examined differences in nest predation. At the smallest spatial scale, we examined nest success in relation to nearest‐neighbor fates and distances for artificial (2000, n=280) and natural nests (2000, n=507). We detected no relationship between nest success and the density of natural nests among fields in any year, nor when we pooled data for all years after controlling for year effects. The proportion of artificial nests that survived also did not depend on experimental nest densities within 1‐ha plots. Overall, 15.0±12.4%, 15.0±11.0%, and 6.2±4.3% of artificial nests survived the 32‐day exposure period in the low, intermediate, and high nest densities, respectively. Additionally, we detected no consistent effect of nearest‐neighbor fate or distance on the success of artificial or natural nests. Thus, our results provide no evidence of density‐dependent predation on duck nests at any scale of analysis, in contrast to a number of previous studies. Variation among geographical locations in the degree to which predation is density‐dependent may reflect the composition of the predator community and the availability of alternate prey.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".