Population Genetic Structure of Three‐Spined Sticklebacks in the St. Lawrence: A Gradient of Change
Bibliographic record
Abstract
ABSTRACT Understanding how environmental gradients shape population genetic structure is critical for elucidating evolutionary dynamics in heterogeneous landscapes. The St. Lawrence Estuary, spanning fluvial, middle, and marine zones, presents a steep salinity gradient that serves as an ideal setting to study such a question. Three‐spined sticklebacks (Gasterosteus aculeatus) thrive across these zones, offering an ideal model system to investigate the interplay of gene flow and natural selection in shaping population structure. Using whole‐genome resequencing of sticklebacks from 12 sites, this study aimed to resolve fine‐scale population structure and investigate how genetic diversity and differentiation are influenced by selection and gene flow. By integrating single nucleotide polymorphisms (SNPs) and structural variants (SVs), we assessed differentiation patterns, examined clinal variation, and evaluated the relative roles of gene flow and selection in shaping population dynamics. Our findings reveal clear genetic differentiation between fluvial and saltwater populations, with Baie‐Saint‐Paul forming a potential third group. Salinity emerged as a key driver of genetic structure, with clinal variation in allele frequencies suggesting ongoing adaptation along the gradient. Demographic modeling indicated a history of secondary contact with recent and weak gene flow. Structural variants, particularly indels, complemented SNP‐based analyses, underscoring their importance in detecting fine‐scale population structure. These results highlight the complex interplay of evolutionary forces shaping biodiversity in transitional environments, providing a basis for exploring local adaptation in connected populations and contributing to broader efforts in conservation genomics.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".