Population assignment tests uncover rare long-distance larval dispersal events
Bibliographic record
Abstract
Long-distance dispersal (LDD) is consequential to metapopulation ecology and evolution. In systems where dispersal is undertaken by small propagules, such as larvae in the ocean, documenting LDD is especially challenging. Genetic parentage analysis has gained traction as a method for measuring larval dispersal, but such studies are generally spatially limited, leaving LDD understudied in marine species. We addressed this knowledge gap by uncovering LDD with population assignment tests in the coral reef fish Elacatinus lori—a species whose short-distance dispersal has been well-characterized by parentage analysis. When adults (n = 931) collected throughout the species’ range were categorized into three source populations, assignment accuracy exceeded 99%, demonstrating low rates of connectivity between populations in the adult generation. After establishing high assignment confidence, we assigned settlers (n = 3,828) to source populations. Within the settler cohort, < 0.1% of individuals were identified as long-distance dispersers from other populations. These results demonstrate an exceptionally low level of connectivity between E. lori populations, despite the potential for ocean currents to facilitate LDD. More broadly, these findings illustrate the value of combining genetic parentage analysis and population assignment tests to uncover short- and long-distance dispersal, respectively.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".