A needle in a haystack: A new metabarcoding approach to survey diversity at the species level of Arcellinida (Amoebozoa: Tubulinea)
Bibliographic record
Abstract
Environmental DNA-based diversity studies have increased in popularity with the development of high throughput sequencing technologies. This permits the potential simultaneous retrieval of vast amounts of molecular data from many different organisms and species, thus contributing to a wide range of biological disciplines. Environmental DNA protocols designed for protists often focused on the highly conserved small subunit of the ribosome gene, that does not permit species-level assignments. On the other hand, eDNA protocols aiming at species-level assignments allow a fine level ecological resolution and reproducible results. These protocols are currently applied to organisms living in marine and shallow lotic freshwater ecosystems, often in a bioindication purpose. Therefore, in this study, we present a species-level eDNA protocol designed to explore diversity of Arcellinida (Amoebozoa: Tubulinea) testate amoebae taxa that is based on mitochondrial cytochrome oxidase subunit I (COI). These organisms are widespread in lentic water bodies and soil ecosystems. We applied this protocol to 42 samples from peatlands, estuaries and soil environments, recovering all the infraorders in Glutinoconcha (with COI data), except for Hyalospheniformes. Our results revealed an unsuspected diversity in morphologically homogeneous groups such as Cylindrothecina, Excentrostoma or Sphaerothecina. With this protocol we expect to revolutionize the design of modern distributional Arcellinida surveys. Our approach involves a rapid and cost-effective analysis of testate amoeba diversity living in contrasted ecosystems. Therefore, the order Arcellinida has the potential to be established as a model group for a wide range of theoretical and applied studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".