Isolation‐by‐distance and genetic parentage analysis provide similar larval dispersal estimates
Bibliographic record
Abstract
Larval exchange among marine populations is a vital driver of population dynamics and has the potential to inform conservation actions, but accurately measuring dispersal remains challenging. Consequently, empirical dispersal kernels have been measured for only a few marine species. Here, we obtained indirect dispersal estimates using an isolation-by-distance (IBD) model in the coral reef fish Elacatinus lori and assessed the accuracy of these estimates by comparing them to direct measurements of dispersal from genetic parentage analysis. Specifically, drawing on the IBD slope and effective population density, we indirectly estimated sigma (σ), the spread of a dispersal distribution. While the spread of the directly measured distribution was σ = 3.93 km (95% CI: 3.29-4.71 km), the spread of the IBD distribution was σ = 4.10 km (95% CI: 3.23-5.03) and σ = 2.90 km (95% CI: 2.26-3.59), assuming a random or monogamous mating system, respectively. Parameterizing Laplace dispersal kernels with these values of σ yielded patterns that were remarkably similar to a kernel fit to the direct parentage data. We also found that, like many marine fishes, E. lori has a large effective population size. However, uncertainty in effective size did not ultimately have a strong effect on the IBD-based dispersal estimates. Taken together, these findings illustrate that accurate dispersal estimates can be produced by indirect IBD methods and suggest that this more feasible approach to estimating dispersal may be broadly applicable to the study of marine larval dispersal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".