Natal Origin Identification of Green Turtles in the North Pacific by Genome-Wide Population Analysis With Limited DNA Samples
Bibliographic record
Abstract
Although studies using genome-wide single nucleotide polymorphisms (SNPs) are growing in non-model species, it is still difficult to prepare sufficient high-quality genomic DNA (gDNA) for population genomic analyses in many wild species. In this study, we first analyzed the population structure of green turtles in the North Pacific; four regions in Japan (the Yaeyama, Okinawa, Amami, and Ogasawara Islands), the Southeast Asia, Taiwan, the Federated States of Micronesia, the Republic Marshall Islands, and the Central or Eastern Pacific (C/E Pacific) using ≃1 ng of gDNA from green turtle field samples—including ones from dead carcasses—and multiplexed inter-simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq). In addition, we explored whether the genome-wide SNP data narrowed down the natal origins of foraging turtles that were difficult to identify by mitochondrial DNA (mtDNA) haplotype alone. The overall nesting population structure observed from genome-wide SNPs was consistent with results from mtDNA, and the population isolation of the C/E Pacific and Ogasawara Islands from the other North Pacific populations was highlighted. However, the population boundaries among the Northwestern Pacific, other than those of the Ogasawaras, were not clear. The uniqueness of the Ogasawara population in genome-wide SNP data enabled the identification of turtles that were more likely to have been born on the Ogasawara Islands. Our results show that genome-wide SNP data are more practical in identifying the natal origins of turtles that were difficult determine by the conventional mtDNA-basis mixed stock analysis. This study also showed that MIG-seq can be expected to meet the potential demand to utilize many preserved or fragmented gDNA samples for population genomics in many marine megafaunas for which sample collections are difficult.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".