Pre-migratory flights in migrant songbirds: the ecological and evolutionary importance of understudied exploratory movements
Bibliographic record
Abstract
Across the animal kingdom, from honeybees to cranes to beavers, exploratory movements to exploit resources, scout prospective territories, or otherwise gain valuable experiences and information that promote fitness have been documented. For example, exploratory movements to investigate potential dispersal targets have been observed in roe deer, Northern cardinals, and tigers alike. However, despite how widespread these movements are, a cohesive definition of exploratory movements has been lacking. We first provide a clear definition of exploratory movements, and use one particular group-migratory songbirds-to catalogue exploratory movements across the annual cycle. The exceptional mobility of migratory songbirds results in exploratory movements not only at a local scale, but also on a regional scale, both in and out of the breeding season. We review the extent to which these movements are made within this group, paying particular attention to how such movements confer fitness benefits, as by securing high-quality territories, prospecting for extra-pair paternity, or even exploiting ephemeral resources. We then zoom in one step further to a particular exploratory movement that has been, to date, almost completely overlooked within this group: that of pre-migratory flights. These flights, which occur during the transitional period between the stationary breeding period and the onset of migration, occur at night and may not be made by all individuals in a population-reasons why these flights have been heretofore critically understudied. We provide the first definition for this behaviour, summarise the current knowledge of this cryptic movement, and hypothesise what evolutionary/ecological advantages conducting it may confer to the individuals that undertake it. As these flights provide experience to the individuals that undertake them, we expect that birds that make pre-migratory flights are better equipped to survive migration (direct fitness benefits) and, due to orientation/navigation abilities, may also reach preferred territories on breeding and wintering grounds faster (indirect fitness benefits). We hope to encourage ecologists to consider such hidden movements in their research concepts and to enhance the framework of movement ecology by this behaviour due to its presumed high biological importance to the annual cycle of birds.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".