Patterns of Nest Predation on Artificial and Natural Nests in Forests
Bibliographic record
Abstract
Abstract: Artificial nest experiments have been used in an attempt to understand patterns of predation affecting natural nests. A growing body of literature suggests that neither relative rates nor patterns of predation are the same for artificial and natural nests. We studied nest predation and daily mortality rates and patterns at real and artificial ground and shrub nests to test the validity of artificial nest experiments. We monitored 1667 artificial and 344 natural nests, over seven trials, in three regions, across 58 sites in Ontario. We controlled for many of the factors thought to be responsible for previously reported differences between predation rates on natural and artificial nests. Although artificial nests in our study resembled natural nests, contained eggs of appropriate size, shape, and color of target bird species, and were placed in similar microhabitats as natural nests, the rates of predation on these nests did not parallel rates on natural nests for any region in terms of absolute rate or pattern. Predation rates on artificial nests did not vary between years, as they tended to for natural nests, and the magnitude of predation pressure on artificial ground nests compared with shrub nests did not show the same pattern as that on natural nests. In general, rates of predation on artificial nests were significantly higher than on natural nests. Our results suggest that conclusions derived from artificial nest studies may be unfounded. Given that many influential ideas in predation theory are based on results of artificial nest experiments, it may be time to redo these experiments with natural nests.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".