Partial diel migration: A facultative migration underpinned by long‐term inter‐individual variation
Bibliographic record
Abstract
The variations in migration that comprise partial diel migrations, putatively occur entirely as a consequence of behavioural flexibility. However, seasonal partial migrations are increasingly recognised to be mediated by a combination of reversible plasticity in response to environmental variation and individual variation due to genetic and environmental effects. Here, we test the hypothesis that while partial diel migration heterogeneity occurs primarily due to short-term within-individual flexibility in behaviour, long-term individual differences in migratory behaviour also underpin this migration variation. Specifically, we use a hierarchical behavioural reaction norm approach to partition within- and among-individual variation in depth use and diel plasticity in depth use, across short- and long-term time-scales, in a group of 47 burbot (Lota lota) tagged with depth-sensing acoustic telemetry transmitters. We found that within-individual variation at the among-dates-within-seasons and among-seasons scale, explained the dominant proportion of phenotypic variation. However, individuals also repeatedly differed in their expression of migration behaviour over the 2 year study duration. These results reveal that diel migration variation occurs primarily due to short-term within-individual flexibility in depth use and diel migration behaviour. However, repeatable individual differences also played a key role in mediating partial diel migration. These findings represent a significant advancement of our understanding of the mechanisms generating the important, yet poorly understood phenomena of partial diel migration. Moreover, given the pervasive occurrence of diel migrations across aquatic taxa, these findings indicate that individual differences have an important, yet previously unacknowledged role in structuring the temporal and vertical dynamics of aquatic ecosystems.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".