Divergent Population Structure in Five Common Rockfish Species of Puget Sound, WA Suggests the Need for Species‐Specific Management
Bibliographic record
Abstract
Quantifying connectivity between endangered or threatened marine populations is critical information for management and conservation, especially where abundance and productivity differ among such populations. Spatial patterns of such connectivity depend not only on extrinsic factors such as oceanography and bathymetry but also on intrinsic species-specific factors such as life history, demography and the location of glacial refugia. Nevertheless, population structure is often inferred from related or ecologically similar species. For example, the population structure in most rockfish species (Sebastes spp.) in the Salish Sea and the US West Coast is currently inferred from genetic data of three species that are known to hybridise in Puget Sound. Here, we determined the population structure and connectivity in five Puget Sound Rockfish species (Black [Sebastes melanops], Yellowtail [S. flavidus], Redstripe [S. proriger], Greenstriped [S. elongatus], and Puget Sound Rockfish [S. emphaeus]) from over 12,000 restriction-site associated DNA sequencing (RADseq) loci. We found species-specific patterns of genetic differentiation, attributable to both extrinsic and intrinsic factors. Specifically, Black and Puget Sound rockfishes showed no genetic differentiation; Yellowtail and Greenstriped rockfishes were structured according to known geographic barriers; and Redstripe Rockfish revealed evidence for temporal genetic differentiation, suggesting irregular recruitment influences population structure. Only Yellowtail Rockfish followed the federal DPS boundaries generally assumed for rockfish, further emphasizing the importance of species-specific management for the effective recovery and management of these rockfish populations and of marine species in general.
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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 | 0.001 |
| 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".