Species identification and connectivity of marine amphipods in Canada’s three oceans
Bibliographic record
Abstract
Monitoring the distribution of marine biodiversity is a crucial step to better assess the impacts of global changes. Arctic marine fauna is dominated by amphipods in terms of abundance and biomass. These peracarids are an important marine order of crustaceans but the number of species found in the different Canadian oceans is currently unknown. Furthermore, most species are difficult to identify due to poor taxonomic descriptions and morphological convergence. The aim of this study was to assess the species diversity of marine amphipods in the three Canadian oceans using DNA barcoding. To do so, we produced a database of DNA barcodes of amphipods from the three Canadian Oceans publicly available from the BOLD website to which we added 310 new sequences from the Canadian Arctic Archipelago. We first delimited amphipod species based on barcode gap detection techniques and tree based method (bPTP) and then compared the composition of amphipods among the three oceans in order to assess the influence of past transarctic exchanges on Arctic diversity. Our analysis of 2309 sequences which represent more than 250 provisional species revealed a high connectivity between the Atlantic and Arctic Oceans. Our results also suggest that a single threshold to delimitate species is not suitable for amphipods. This study highlights the challenges involved in species delimitation and the need to obtain complete barcoding inventories in marine invertebrates.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| 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".