Historical demography and genetic differentiation of the giant freshwater prawn <i>Macrobrachium rosenbergii</i> in Bangladesh based on mitochondrial and dd<scp>RAD</scp> sequence variation
Bibliographic record
Abstract
Abstract Macrobrachium rosenbergii, the giant freshwater prawn, is an important source of high quality protein and occurs naturally in rivers as well as commercial farms in South and South‐East Asia, including Bangladesh. This study investigated the genetic variation and population structure of M. rosenbergii sampled from four rivers in Bangladesh (sample size ranged from 19 to 20), assessing sequence variation, both in the mitochondrial cytochrome oxidase subunit 1 (CO1) gene and in 106 single nucleotide polymorphisms (SNPs) sampled randomly from the genome with double digest RAD sequencing (ddRADseq). The mitochondrial variation presented a shallow genealogy with high haplotype diversity (h = 0.95), reflecting an expansion in population size for the last ~82 kyr. Based on the CO1 variation the current effective population size (Ne) was 9.7 × 106 (CI: 1.33 × 106 – 35.84 × 106) individuals. A significant population differentiation was observed with the mitochondrial CO1 sequence variation and based on the ddRADseq variation, which could be traced to the divergence of the population in the Naf River in the South‐East border with Myanmar from the other populations. A differentiation in mtDNA haplotype frequencies was also observed between the Biskhali River and the Karnaphuli Rivers in eastern Bangladesh. This study demonstrated the use of high‐throughput genotyping based on the ddRADseq method to reveal population structure at a small geographical scale for an important freshwater prawn. The information from this study can be utilized for management and conservation of this species in Bangladesh.
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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".