DNA barcoding of southern African crustaceans reveals a mix of invasive species and potential cryptic diversity
Bibliographic record
Abstract
Globally, crustaceans represent one of the most taxonomically diverse and economically important invertebrate group. Notwithstanding, the diversity within this group is poorly known because most crustaceans are often associated with varied habits, forms, sizes and habitats; making species identification by conventional methods extremely challenging. In addition, progress towards understanding the diversity within this group especially in southern Africa has been severely hampered by the declining number of trained taxonomists, the presence of invasive alien species, over exploitation, etc. However, the advent of molecular techniques such as "DNA barcoding and Metabarcoding" can accelerate species identification and the discovery of new species. To contribute to the growing body of knowledge on crustacean diversity, we collected data from five southern African countries and used a DNA barcoding approach to build the first DNA barcode reference library for southern African crustaceans. We tested the reliability of this DNA barcode reference library to facilitate species identification using two approaches. We recovered high efficacy of specimen identification/discrimination; supported by both barcode gap and tree-base species identification methods. In addition, we identified alien invasive species and specimens with 'no ID" in our DNA barcode reference library. The later; highlighting specimens requiring (i) further investigation and/or (ii) the potential presence of cryptic diversity or (iii) misidentifications. This unique data set although with some sampling gaps presents many opportunities for exploring the effect and extent of invasive alien species, the role of the pet trade as a pathway for crustacean species introduction into novel environments, sea food authentication, phylogenetic relationships within the larger crustacean groupings and the discovery of new 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".