Data from: Unraveling hierarchical genetic structure in a marine metapopulation: a comparison of three high-throughput genotyping approaches
Bibliographic record
Abstract
Marine metapopulations often exhibit subtle population structure that can be difficult to detect. Given recent advances in high-throughput sequencing, an emerging question is whether various genetic approaches, in concert with improved sampling designs, will substantially improve our understanding of genetic structure in the sea. To address this question, we explored hierarchical patterns of structure in the coral reef fish Elacatinus lori using a high-resolution approach with respect to both genetic and geographic sampling. Previously, we identified three putative E. lori populations within Belize using traditional genetic markers and sparse geographic sampling: barrier reef and Turneffe Atoll; Glover’s Atoll; and Lighthouse Atoll. Here, we systematically sampled individuals at ~10 km intervals throughout these reefs (1,129 individuals from 35 sites) and sequenced all individuals at three sets of markers: 2,418 SNPs; 89 microsatellites; and 57 non-repetitive nuclear loci. At broad spatial scales, the markers were consistent with each other and with previous findings. At finer spatial scales, there was new evidence of genetic substructure, but our three marker sets differed slightly in their ability to detect these patterns. Specifically, we found subtle structure between the barrier reef and Turneffe Atoll, with SNPs resolving this pattern most effectively. We also documented isolation by distance within the barrier reef. Sensitivity analyses revealed that the number of loci (and alleles) had a strong effect on the detection of structure for all three marker sets, particularly at small spatial scales. Taken together, these results illustrate empirically that high-throughput genotyping data can elucidate subtle genetic structure at previously-undetected scales in a dispersive marine fish.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".