Population Genomics of <i>P. miniata</i> along the Pacific Coastline Reveal Subtly Diverging Genomics Along an Extensive Range Gap
Bibliographic record
Abstract
Abstract Many marine species exhibit range gaps, patchy distributions, or genetic disjunctions throughout their ranges. These discontinuities can result from various interacting mechanisms, though directly linking ecological and historical factors to observed distributions or genetic disjunctions often proves challenging. Intriguingly, substantial research has demonstrated that these spatial and genetic discontinuities frequently occur in species with long-lived planktonic larvae, which possess the capacity for extensive oceanic dispersal. This study investigates the population genetics of one such species, the bat star Patiria miniata . Despite its long-lived planktonic larval stage of six to ten weeks, P. miniata maintains both a range gap and a, geographically separate, strong genetic disjunction throughout its distribution from Alaska to Baja California. Utilizing low-coverage whole-genome sequencing of over 200 individuals collected throughout P. miniata ’s range between the early 2000s and 2023, we corroborate previous findings of a significant genetic disjunction across Queen Charlotte Sound, north of Vancouver Island. Additionally, we present new evidence of strong divergence at several genomic loci across an extensive range gap in Washington and Oregon, despite subtle genetic population structure at most loci here. Such differences may reflect more recent ecological or oceanographic barriers, rather than historical processes. Our results demonstrate that while marine invertebrate populations may appear panmictic based on genome-wide metrics of population structure alone, strong local selection for specific gene segments may be maintained in some populations. This research contributes to our understanding of the complex interplay between dispersal potential and local adaptation in marine ecosystems and highlights the importance of considering both genetic structure and finer-scale adaptation in coastal marine populations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".