Putatively adaptive genetic variation in the giant California sea cucumber (<i>Parastichopus californicus</i>) as revealed by environmental association analysis of restriction‐site associated DNA sequencing data
Bibliographic record
Abstract
Understanding the spatial scale of local adaptation and the factors associated with adaptive diversity are important objectives for ecology and evolutionary biology, and have significant implications for effective conservation and management of wild populations and natural resources. In this study, we used an environmental association analysis to identify important bioclimatic variables correlated with putatively adaptive genetic variation in a benthic marine invertebrate-the giant California sea cucumber (Parastichopus californicus)-spanning coastal British Columbia and southeastern Alaska. We used a redundancy analysis (RDA) with 3,699 single nucleotide polymorphisms (SNPs) obtained using RAD sequencing to detect candidate markers associated with 11 bioclimatic variables, including sea bottom and surface conditions, across two spatial scales (entire study area and within subregions). At the broadest scale, RDA revealed 59 candidate SNPs, 86% of which were associated with mean bottom temperature. Similar patterns were identified when population structure was accounted for. Additive polygenic scores, which provide a measure of the cumulative signal across all candidate SNPs, were strongly correlated with mean bottom temperature, consistent with spatially varying selection across a thermal gradient. At a finer scale, 23 candidate SNPs were detected, primarily associated with surface salinity (26%) and bottom current velocity (17%). Our findings suggest that environmental variables may play a role as drivers of spatially varying selection for P. californicus. These results provide context for future studies to evaluate the genetic basis of local adaptation in P. californicus and help inform the relevant scales and environmental variables for in situ field studies of putative adaptive variation in marine invertebrates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".