Risk of Nest Predation Influences Reproductive Investment in American Kestrels (Falco sparverius): an Experimental Test
Bibliographic record
Abstract
Nest predation is the primary cause of nest failure in birds. Individuals should therefore adjust parental investment to minimize the costs associated with this constraint; evidence suggests that nest predation influences nest-site selection, and drives variation in both clutch size and parental behavior. Here, we test how the perception of the risk of nest predation from red squirrels (Tamiasciurus hudsonicus) influenced nest-site selection and reproductive investment of American Kestrels (Falco sparverius) breeding in the boreal forest. For this purpose, we conducted audio playbacks of squirrel vocalizations and altered nest boxes to experimentally increase cues of the presence of Red Squirrels in the vicinity of potential nests. Experimental manipulations of the risk of nest predation did not influence nest-site selection; however, experimentally increasing the perceived risk of nest predation induced kestrels to initiate breeding later, and to lay larger clutches. Parents did not appreciably alter incubation behavior in response to our manipulation, although the duration of incubation was longer where natural squirrel threat was higher. Our results showed that kestrels are capable of making facultative adjustments to current reproductive investment in response to their perception of the risk of nest predation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".