DNA barcodes for Cladocera and Copepoda from Mexico and Guatemala, highlights and new discoveries
Bibliographic record
Abstract
DNA barcoding, based on sequence diversity in the mitochondrial COI gene, has proven an excellent tool for identifying species in many animal groups. Here, we report the first barcode studies for freshwater zooplankton from Mexico and Guatemala and discuss the taxonomic and biological implications of this work. Our studies examined 61 species of Cladocera and 21 of Copepoda, about 40% of the known fauna in this region. Sequence divergences among conspecific individuals of cladocerans and copepods averaged 0.82% and 0.79%, respectively, while sequence divergences among congeneric taxa were on average 15-20 times as high. Barcodes were successful in discriminating all species in our study, but sequences for Mexican Daphnia exilis overlapped with those of D. spinulata from Argentina. Our barcode data revealed evidence of many species overlooked by current classification systems —for example, based on COI genotypes the Diapahanosoma birgei group appears to include 5 species, while Ceriodaphnia cf. rigaudi, Moina cf. micrura, Mastigodiaptomus albuquerquensis and Mastigodiaptomus reidae all include 2–3 taxa. The barcode results support recent taxonomic revisions, such as recognition of the genus Leberis, and the presence of several species in the D. birgei and Chydorus sphaericus complexes. The present results indicate that DNA barcoding will provide powerful new insights into both the incidence of cryptic species and a better understanding of zooplankton distributions, aiding evaluation of the factors influencing competitive outcomes, and the colonization of aquatic environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".