Experimental tests of a seasonally changing visual preference for habitat in a long‐distance migratory shorebird
Bibliographic record
Abstract
Abstract Migratory shorebirds show highly organized seasonal cycles in physiological and morphological traits (body mass and composition, plumage, hormone levels, etc.), which in captivity is accompanied by restless behaviour at times when free‐living birds would start migration. We introduce the idea that seasonally changing preference for habitat could motivate migrants to embark on migration and that this cognitive process could also guide them to seasonally appropriate places. We explored this by testing whether red knots (Calidris canutus), which also in captivity maintain marked circannual phenotypic rhythms, show evidence of seasonal change in preference for pictures of seasonally appropriate habitats. We first developed a method to verify whether red knots are able to memorize and discriminate contrasting pictures projected by LCD projectors. This was followed by two different experiments in which we tested for a seasonally changing preference for breeding or non‐breeding habitat. When carried out during the pre‐breeding season, the red knots are expected to prefer pictures of mudflats, their non‐breeding habitat. At the start of the breeding season, they should prefer pictures of the tundra breeding habitat. We established that knots are able to distinguish and memorize projected images. We failed to demonstrate the predicted change in vision‐based habitat preference, but for reasons of test design we do not interpret this as a strong rejection of the hypothesis. Instead, we suggest that experiments with greater numbers of individuals tested once, perhaps in combination with the provision of additional cues such as smells and sounds, will help the development of these ideas further.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".