Data from: Landscape genetic structure of Scirpus mariqueter reveals a putatively adaptive differentiation under strong gene flow in estuaries
Bibliographic record
Abstract
Estuarine organisms grow in highly heterogeneous habitats and their genetic differentiation are driven by selective and neutral processes as well as population colonization history. However, the relative importance of the processes that underlie genetic structure is still puzzling. Scirpus mariqueter is a perennial grass almost limited in the Changjiang River estuary and it’s adjacent Qiantang River estuary. Here, using amplified fragment length polymorphism (AFLP), a moderate-high level of genetic differentiation among populations (range FST: 0.0310-0.3325) was showed despite large ongoing dispersal. FLOCK assigned all individuals to 13 clusters and revealed a complex genetic structure. Some genetic clusters were limited in peripheries compared with very mixing constitution in centre populations, suggesting local adaptation was more likely to occur in peripheral populations. 21 candidate outliers under positive selection were detected, and further, the differentiation patterns correlated with geographic distance, salinity difference and colonization history were analyzed with or without the outliers. Combined results of AMOVA and IBD based on different dataset, it was found that the effects of geographic distance and population colonization history on isolation seemed to be promoted by divergent selection. However, none-liner IBE pattern indicates the effects of salinity were overwhelmed by spatial distance or other ecological process in certain areas, and also suggests that salinity was not the only selective factor driving population differentiation. These results together indicate that geographic distance, salinity difference and colonization history co-contributed in shaping the genetic structure of S. mariqueter, and that their relative importance was correlated with spatial scale and environment gradient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.005 |
| 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 teacher head, 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".