Genetic Diversity Assessment of Island Endemic Species Using Environmental DNA Metabarcoding
Bibliographic record
Abstract
The geographic isolation, coupled with small population sizes and the growing anthropogenic demands, makes island endemic species highly susceptible to genetic erosion. Conventional biodiversity evaluation techniques tend to be invasive, time-consuming, and spatially restricted, and thus, there is a need for scalable, non-invasive techniques. The paper tests the usefulness of environmental DNA (eDNA) metabarcoding for measuring genetic diversity among various endemic taxa on an island. Five ecologically distinct sites on the island (n = 135) were sampled for water and soil samples, after which high-throughput sequencing was performed on the mitochondrial COI and 12S rRNA gene regions. A bioinformatics analysis reported 92 distinct operational taxonomic units (OTUs), with an average sequencing depth per sample of 45,000 reads and a high detection sensitivity (>92). The genetic diversity indices showed an average to low level of heterozygosity (He = 0.22–0.71) in most species and a great reduction (about 35%) in the high disturbance of human beings (p < 0.01). Molecular variance (AMOVA) was analyzed, revealing that 62% of genetic variation was within populations and 38% was differentiated among the islands. In addition, rare or cryptic species accounted for almost one-fifth of total detections, indicating that the method is sensitive. Results indicate that eDNA metabarcoding is a powerful, non-invasive, and high-resolution technique for monitoring genetic diversity in delicate island ecosystems. The strategy has significant potential to advance conservation measures by identifying genetic bottlenecks and biodiversity degradation early. Altogether, the research highlights the potential of combining molecular methods with ecological surveillance to contribute to the sustainable management and conservation of endemic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 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".