Deceptive nest defence in ground-nesting birds and the risk of intermediate strategies
Bibliographic record
Abstract
Nest predation is an important determinant of reproductive success and ground-nesting birds exhibit a variety of nest defence strategies to mitigate the risk. Many small-bodied, ground nesting birds rely on deceptive behaviours such as injury-feigning to reduce nest predation: we call this behaviour active deception. However, active deception may entail risks to adults, and passive deceptive behaviour, where individuals effectively sneak away from the nest by flushing at long distances, may be an alternative means of avoiding nest predation. We provide a simple model to demonstrate that these tails of the flush distance distribution could minimize predation risk; an intermediate strategy of moderate flush distances means that birds flush more often than with short-distance flushes, and once flushed, the nest is more easily located than for long-distance flushes. We tested this model using two species of ground nesting shorebirds, the White-rumped Sandpiper (Calidris fuscicollis) and the Red Phalarope (Phalaropus fulicarius). We demonstrate that short-distance flushes are associated with active deception and intermediate-distance flushes are associated with an increased risk of nest predation. However, we found no evidence that this potential selective pressure against intermediate strategies has produced a bimodal distribution of nest defence traits. The heritability of defence behaviours, or the ability of individuals to learn, is unknown and other factors such as energetic constraints or risks to adults might also influence flush distances and defence behaviours.
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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 | 0.002 |
| 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 | 0.001 | 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".