Low diversity, little genetic structure but no inbreeding in a high-density island endemic pit-viper<i>Gloydius shedaoensis</i>
Bibliographic record
Abstract
Abstract Island species and their ecosystems play an important role in global biodiversity preservation, and many vulnerable island species are conservation priorities. Although insular habitat likely facilitates the species diversification process, it may also aggravate the fragility of these species with high risk of inbreeding. The Shedao pit-viper Gloydius shedaoensis is an island endemic species with an extremely high population density, which has been categorized as vulnerable in the IUCN (International Union for the Conservation of Nature and Natural Resources) Red List. We collected 13,148 SNP (Single Nucleotide Polymorphism) from across its genome and examined its genetic diversity and demographic history. The Shedao pit-viper has a low genetic diversity but shows no sign of inbreeding. Furthermore, population genetic structure analysis, including the neighbor-joining tree, principal coordinate analysis, clustering, and spatial autocorrelation, revealed a general lack of spatial structure. Only the isolation by distance residues suggested a weak patchiness. Overall, the population is nearly panmictic and gene flow is evenly distributed across the island. A large number of individuals, small size of the island, and the lack of population structure likely all contribute to the lack of inbreeding in this species. We also detected signs of male-biased dispersal, which likely is another inbreeding avoidance strategy. Historical demographic analysis suggested that the historical population size and distribution of the species are much larger than their current ones. The multiple transgressive–regressive events since the Late Pleistocene are likely the main cause of the population size changes. Taken together, our results provide a basic scientific foundation for the conservation of this interesting and important species.
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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 | 0.000 |
| 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".