Anostracans muddy the water: 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 enable colonization of new areas. Like many other aquatic invertebrates, Anostraca (Crustacea) disperse passively as dormant fertilized eggs. Here, we first study the genetic diversity of <i>Branchinecta</i> fairy shrimps (identified morphologically as <i>B. readingi</i>) inhabiting lakes in Saskatchewan and British Columbia, Canada. Secondly, we attempt to reconstruct the phylogeny and dispersal events that explain their genetic distribution. Finally, we compare genetic diversity and distribution of <i>Branchinecta</i> sp. and <i>Artemia franciscana</i>, the two anostracans inhabiting the Saskatchewan lakes but with different salinity niches. To this aim, we sequenced CO1 and 16S gene fragments of the <i>Branchinecta</i> individuals collected from eight Saskatchewan lakes. We found a relatively high number of haplotypes for both markers (79 in CO1 and 76 in 16S) in <i>Branchinecta</i> from Saskatchewan. Three genetic clades at the CO1 gene region were found in Saskatchewan <i>Branchinecta</i>, with one of these clades being widely recorded previously in the USA (but described as <i>B. mackini</i>). In contrast, 54 CO1 haplotypes were previously recorded in <i>A. franciscana</i> in Saskatchewan for which all CO1 sequences belonged to one genetic clade. This suggests that <i>Branchinecta</i> sequences might be originating from several distinct Ice Age refugia, contrary to <i>A. franciscana</i> which might be originating 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 <i>Branchinecta</i>. However, we found no genetic support for a distinction between <i>B. readingi</i> and <i>B. mackini</i>.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".