Animal dispersal costs are not universal
Bibliographic record
Abstract
Dispersal is a keystone process shaping ecological and evolutionary dynamics, often assumed to be inherently costly. We synthesized 696 effect sizes from 206 studies across 148 animal species, spanning all continents and ecosystems, to test this assumption. Contrary to long-standing dogma, we found no overall effect of dispersal on fitness (mean effect size: -0.03, 95% CIs: -0.09 to 0.03). No tested biological or methodological moderators explained this variation. Instead, heterogeneity was highest within studies, suggesting that dispersal is highly context-dependent within studies and species. These findings align with game-theoretic expectations that dispersal and philopatry are alternative strategies maintained by balancing or frequency-dependent selection. Our findings are consistent with the view that dispersal involves a balancing act between strategies that yield equivalent long-term payoffs across variable conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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; both teacher heads agree on what is shown here.
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".