Long‐distance migrants vary migratory behaviour as much as short‐distance migrants: An individual‐level comparison from a seabird species with diverse migration strategies
Bibliographic record
Abstract
As environmental conditions fluctuate across years, seasonal migrants must determine where and when to move without comprehensive knowledge of conditions beyond their current location. Animals can address this challenge by following cues in their local environment to vary behaviour in response to current conditions, or by moving based on learned or inherited experience of past conditions resulting in fixed behaviour across years. It is often claimed that long-distance migrants are more fixed in their migratory behaviour because as distance between breeding and wintering areas increases, reliability of cues to predict distant and future conditions decreases. While supported by some population-level studies, the influence of migration distance on behavioural variation is seldom examined on an individual level. Lesser black-backed gulls Larus fuscus are generalist seabirds that use a diversity of migration strategies. Using high-resolution multi-year GPS tracking data from 82 individuals from eight colonies in Western Europe, we quantified inter- and intra-individual variation in non-breeding distributions, winter site fidelity, migration routes and timing of migration, with the objectives of determining how much variation lesser black-backed gulls have in their migratory behaviour and examining whether variation changes with migration distance. We found that intra-individual variation was significantly lower than variation between individuals for non-breeding distributions, winter site fidelity, migration routes and timing of migration, resulting in consistent individual strategies for all behaviours examined. Yet, intra-individual variation ranged widely among individuals (e.g. winter site overlap: 0-0.91 out of 1; migration timing: 0-192 days), and importantly, individual differences in variation were not related to migration distance. The apparent preference for maintaining a consistent strategy, present in even the shortest distance migrants, suggests that familiarity may be more advantageous than exactly tracking current environmental conditions. Yet, variation in behaviour across years was observed in many individuals and could be substantial. This suggests that individuals, irrespective of migration distance, have the capacity to adjust to current conditions within the broad confines of their individual strategies, and occasionally, even change their strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".