Population genomics reveals strong impacts of genetic drift without purging and guides conservation of bull and giant kelp
Bibliographic record
Abstract
Summary Kelp forests are declining in many parts of the northeast Pacific 1–4 . In small populations, genetic drift can reduce adaptive variation and increase fixation of recessive deleterious alleles 5–7 , but natural selection may purge harmful variants 8–10 . To understand evolutionary dynamics and inform restoration strategies, we investigated genetic structure and the outcomes of genetic drift and purging by sequencing the genomes of 429 bull kelp ( Nereocystis luetkeana ) and 211 giant kelp ( Macrocystis pyrifera sensu lato 11 ; cf. 12 ) from the coastlines of British Columbia and Washington. We identified 6-7 geographically and genetically distinct clusters in each species. Low effective population size was associated with low genetic diversity and high inbreeding coefficients (including increased selfing rates), with extreme variation in these genetic health indices among bull kelp populations but more moderate variation in giant kelp. We found no evidence that natural selection is purging putative recessive deleterious alleles in either species. Instead, genetic drift has fixed many such alleles in small populations of bull kelp, leading us to predict (1) reduced within-population inbreeding depression in small populations, which may be associated with an observed shift toward increased selfing rate, and (2) hybrid vigour in crosses between small populations. Our genomic findings imply several strategies for optimal sourcing and crossing of populations for restoration and aquaculture, but which require experimental validation. Overall, our work reveals strong genetic structure and suggests that conservation strategies should consider the multiple health risks faced by small populations whose evolutionary dynamics are dominated by genetic drift.
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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 | 0.000 | 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) | 0.001 | 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".