Assessing Temporal Patterns and Species Composition of Glass Eel (Anguilla spp.) Cohorts in Sumatra and Java Using DNA Barcodes
Bibliographic record
Abstract
Anguillid eels are widely acknowledged for their ecological and socio-economic value in many countries. Yet, knowledge regarding their biodiversity, distribution and abundance remains superficial—particularly in tropical countries such as Indonesia, where demand for anguillid eels is steadily increasing along with the threat imposed by river infrastructure developments. We investigated the diversity of anguillid eels on the western Indonesian islands of Sumatra and Java using automated molecular classification and genetic species delimitation methods to explore temporal patterns of glass eel cohorts entering inland waters. A total of 278 glass eels were collected from monthly samplings along the west coast of Sumatra and the south coast of Java between March 2017 and February 2018. An automated, DNA-based glass eel identification was performed using a DNA barcode reference library consisting of 64 newly generated DNA barcodes and 117 DNA barcodes retrieved from BOLD for all nine Anguilla species known to occur in Indonesia. Species delimitation methods converged in delineating eight Molecular Operational Taxonomic Units (MOTUs), with A. nebolusa and A. bengalensis being undistinguishable by DNA barcodes. A total of four MOTUs were detected within the glass eel samples, corresponding to Anguilla bicolor, A. interioris, A. marmorata, and A. nebulosa/A. bengalensis. Monthly captures indicated that glass eel recruitment peaks in June, during the onset of the dry season, and that A. bicolor is the most prevalent species. Comparing indices of mitochondrial genetic diversity between yellow/silver eels, originating from several sites across the species range distribution, and glass eels, collected in West Sumatra and Java, indicated a marked difference. Glass eels displayed a much lower diversity than yellow/silver eels. Implications for the management of glass eel fisheries and species conservation are discussed.
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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".