Whole genome analyses of the endangered Northern abalone (Haliotis kamtschatkana) reveal population differentiation and a genomic signature of a dramatic population decline
Bibliographic record
Abstract
Despite widespread declines of many wildlife species, the effects of population decline on the genetic health and the recovery potential of affected species is still poorly understood, especially beyond a few charismatic species. The Northern abalone (or Pinto abalone; Haliotis kamtschatkana) is a marine gastropod mollusc of social, cultural and historical economic importance in the Pacific Northwest of North America that experienced a decline in population density due to commercial harvest and is currently listed as endangered in Canada under the Species at Risk Act. Previous genetic investigations based on microsatellites and reduced-representation approaches concluded that Northern abalone is panmictic throughout its range, from Alaska to California, and identified high levels of genetic variation with no indication of population decline. Using whole genome resequencing data from Northern abalone sampled across the northern part of the species range, we instead identified both: 1) significant differentiation between two genetic groups, albeit very concentrated in the genome; and 2) a strong signature of a dramatic population decline, without evidence of genetic inbreeding. Even though demographic reconstructions showed a timid signal of recent population expansion, the pervasive excess of rare alleles identified (including a high occurrence of singletons) may pose a genetic load risk, potentially hindering the species recovery. We also found evidence of historical, rather than current, connectivity throughout the area investigated. These results are important for management decisions and highlight the utility of whole genome data in conservation, especially in species with historically large effective population sizes like the Northern abalone.
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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 | 0.000 | 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) | 0.001 | 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".