Can genetic diversity in microalgae species be explained by climate: an overview of metabarcoding with diatoms
Bibliographic record
Abstract
Diatoms, a diverse and abundant group of microalgae, play a crucial role in the functioning of rivers, and are widely used as indicators of ecological quality. This microalgae group has an intraspecific genetic diversity that is poorly understood on a global scale. We examined their genetic diversity using metabarcoding data from Nordic to Equatorial rivers (n = 1103 samples). Notably, 61% of genetic variants were endemic to a single climate zone, including 33% from the Equatorial zone. Looking at the genetic diversity within species, one third of the species showed geographic pattern between climate zones and the phylogenetic structure of their communities indicated that they were shaped by environmental filtering. Another third showed no geographic pattern, and their communities were in majority shaped by neutral processes. A final group was between these two situations. Interestingly, no geographic pattern was observed within the same climate zones, even in regions over 10 000 km apart. We conclude that the numerous species showing allopatric diversification between climate zones, would deserve to be separated into new species to improve diatom-based biomonitoring tools. For future studies, expanding geographical sampling coverage, together with using multi-markers or metagenomes approaches would enable to go beyond these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".