POPULATION GENETIC STRUCTURE OF TWO CRYPTIC DUCKWEED SPECIES (Lemna minor & L. turionifera) IN ALBERTA USING A GENOTYPING-BY-SEQUENCING APPROACH
Bibliographic record
Abstract
Identifying the population genetic structure is important for the development of species-specific management plans. Investigating the population genetics of cryptic species is even more critical. Here we focus on two cryptic duckweed species, Lemna minor L. and L. turionifera Landolt, which have overlapping ranges in our study region of Alberta, Canada, and elsewhere, and are easily mistaken for one another. We used genotyping-by-sequencing to determine the population genetic structure of both duckweed species. A total of 192 samples were sequenced and after filtering, >16,000 SNP were used to examine patterns of genetic diversity between and within L. minor and L. turionifera. The two species showed clear differentiation. When examining L. turionifera singly, we found no evidence of genetically distinct populations among 67 samples from 43 sites. In contrast, when examining L. minor singly, we discovered at least three genetically distinct populations among the 30 samples from eight sites, even though these were from a small geographic area. We also examined the relationship between surface water quality variables and the distribution of the two Lemna species. The sites containing L. turionifera had a wider range of water chemistry variables suggesting they are more tolerant of different environmental conditions. In contrast, each of the three genetically distinct L. minor groups had different water chemistry profiles. Large differences between L. minor and L. turionifera in their regional distributions and degrees of genetic differentiation highlight the importance of documentation and careful monitoring of Lemna species within Alberta, and in other regions where they co-occur.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".