Proximate drivers of migration propensity: a meta-analysis across species
Bibliographic record
Abstract
Animal migration is multifaceted in nature, but the relative strength of different cues that trigger resulting patterns of migration is not well understood. Partially migratory populations offer an opportunity to test hypotheses about migration more broadly by comparing trait differences of migrants and residents. We quantitatively reviewed 45 studies that statistically modeled migration propensity, extracting132 effect sizes for internal and external proximate drivers across taxa. Our meta-analysis revealed that internal and external drivers had medium (Cohen’s d > 0.3) and large (Cohen’s d > 0.5) effect sizes on migration propensity respectively. Predator abundance and predation risk had a large effect, as did individual behaviour (e.g., personality). The abiotic environment and individual physiology had a medium effect on migration propensity. Of the studies that examined genetic divergence between migrants and residents, 64% found some genetic divergence between groups. These results clarify broad proximate drivers of migration and offer generalities across taxa.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.032 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".