Molecular Species Delimitation and Biogeography of Canadian Marine Planktonic Crustaceans
Bibliographic record
Abstract
Zooplankton are a major component of the marine environment in both diversity and biomass and are a crucial source of nutrients for organisms at higher trophic levels. Unfortunately, marine zooplankton biodiversity is not well known because of difficult morphological identifications and lack of taxonomic experts for many groups. In addition, the large taxonomic diversity present in plankton and low sampling coverage pose challenges in obtaining a better understanding of true zooplankton diversity. Molecular identification tools, like DNA barcoding, have been successfully used to identify marine planktonic specimens to a species. However, the behaviour of methods for specimen identification and species delimitation remain untested for taxonomically div¬erse and widely-distributed marine zooplanktonic groups. Using Canadian marine planktonic crustacean collections, I generated a multi-gene data set including COI-5P and 18S-V4 molecular markers of morphologically-identified Copepoda and Thecostraca (Multicrustacea: Hexanauplia) species. I used this data set to assess generalities in the genetic divergence patterns and to determine if a barcode gap exists separating interspecific and intraspecific molecular divergences, which can reliably delimit specimens into species. I then used this information to evaluate the North Pacific, Arctic, and North Atlantic biogeography of marine Calanoida (Hexanauplia: Copepoda) plankton. My work provides essential information on the molecular data and analysis tools necessary to conduct rapid biodiversity surveys on marine plankton, facilitating the exploration of unknown faunas using DNA barcoding and providing baseline data against which future comparisons can be made.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".