Fine-scale seascape genomics of an exploited marine species, the common cockle <i>Cerastoderma edule</i> , using a multi-modelling approach
Bibliographic record
Abstract
ABSTRACT Population dynamics of marine species that are sessile as adults are driven by oceanographic dispersal of larvae from spawning to nursery grounds. This is mediated by life-history traits such as the timing and frequency of spawning, larval behaviour and duration, and settlement success. Here, we use 1725 single nucleotide polymorphisms (SNPs) to study the fine scale spatial genetic structure in the commercially important cockle species Cerastoderma edule and compare it to environmental variables and current-mediated larval dispersal within a modelling framework. Hydrodynamic modelling employing the NEMO Atlantic Margin Model (AMM15) was used to simulate larval transport and estimate connectivity between populations during spawning months (April - September), factoring in larval duration and seasonal variability of ocean currents. Results at neutral loci reveal the existence of three separate genetic clusters (mean F ST =0.021) within a relatively fine spatial scale in the northwest Atlantic. Environmental association (EA) analysis indicates that oceanographic currents and geographical distance between the populations explain over 20% of the variance observed at neutral loci, while genetic variance (71%) at outlier loci was explained by sea surface temperatures extremes. These results fill an important knowledge gap in the management of a commercially important, overexploited species, and bring us closer to understanding the role of larval dispersal in connecting populations at a fine geographical scale.
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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 | 0.000 | 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) | 0.000 | 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".