Seascape genomics provides evidence for thermal adaptation and current‐mediated population structure in American lobster (<i>Homarus americanus</i>)
Bibliographic record
Abstract
Investigating how environmental features shape the genetic structure of populations is crucial for understanding how they are potentially adapted to their habitats, as well as for sound management. In this study, we assessed the relative importance of spatial distribution, ocean currents and sea surface temperature (SST) on patterns of putatively neutral and adaptive genetic variation among American lobster from 19 locations using population differentiation (PD) approaches combined with environmental association (EA) analyses. First, PD approaches (using bayescan, arlequin and outflank) found 28 outlier SNPs putatively under divergent selection and 9770 neutral SNPs in common. Redundancy analysis revealed that spatial distribution, ocean current-mediated larval connectivity and SST explained 31.7% of the neutral genetic differentiation, with ocean currents driving the majority of this relationship (21.0%). After removing the influence of spatial distribution, no SST were significant for putatively neutral genetic variation whereas minimum annual SST still had a significant impact and explained 8.1% of the putatively adaptive genetic variation. Second, EA analyses (using Pearson correlation tests, bayescenv and lfmm) jointly identified seven SNPs as candidates for thermal adaptation. Covariation at these SNPs was assessed with a spatial multivariate analysis that highlighted a significant temperature association, after accounting for the influence of spatial distribution. Among the 505 candidate SNPs detected by at least one of the three approaches, we discovered three polymorphisms located in genes previously shown to play a role in thermal adaptation. Our results have implications for the management of the American lobster and provide a foundation on which to predict how this species will cope with climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".