Anostracans from North American lakes show unclear phylogenetic relationships between taxa and distinctive dispersal patterns
Bibliographic record
Abstract
Dispersal is essential for all organisms to colonize new areas. Like many other aquatic invertebrates, Anostraca (Crustacea) disperse passively as dormant fertilized eggs. Here, we first study the genetic diversity of Branchinecta fairy shrimps (identified morphologically as B. readingi) inhabiting lakes in Saskatchewan and British Columbia, Canada. Second, we attempt to reconstruct the phylogeny and dispersal events that explain their genetic distribution. Finally, we compare genetic diversity and distribution of Branchinecta sp. and Artemia franciscana, the 2 anostracans inhabiting the Saskatchewan lakes but with different salinity niches. To this aim, we sequenced CO1 and 16S gene fragments of the Branchinecta individuals collected from 8 Saskatchewan lakes. We found a relatively high number of haplotypes for both markers (79 in CO1 and 76 in 16S) in Branchinecta from Saskatchewan. Three genetic clades at the CO1 gene region were found in Saskatchewan Branchinecta, one of which was widely recorded previously in the United States (but described as B. mackini). By contrast, 54 CO1 haplotypes were previously recorded in A. franciscana in Saskatchewan, with all CO1 sequences belonging to one genetic clade, suggesting that Branchinecta sequences might originate from several distinct Ice Age refugia, contrary to A. franciscana, which might originate from a single refuge. High density of suitable habitats (>100 000 lakes in Saskatchewan), weak priority effects, and frequent dispersal events could explain high genetic diversity found in the studied Branchinecta. However, we found no genetic support for a distinction between B. readingi and B. mackini.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".