eDNA metabarcoding as a means to assess distribution of subterranean fish communities: Iranian blind cave fishes as a case study
Bibliographic record
Abstract
One of the most important steps in conservation of the subterranean life forms is to decipher their distribution and ecology, which is challenging using traditional approaches. Development of an environmental DNA (eDNA) assay provides an efficient means for discovering and monitoring subterranean life forms. In this study, the distribution of three Iranian blind cave fish species (blind Iran cave barb Garra typhlops, blind Lorestan cave barb Garra lorestanensis, and blind cave loach Eidinemacheilus smithi) was assessed using 12S rRNA gene eDNA metabarcoding performed using MiFish-U PCR primers and preliminary species distribution modelling (SDM) using bioclimatic data. The majority of sampling localities with positive detection of cave barb eDNA fall within suitable habitats in the Zagros Mountains of Iran. Our results revealed that Lorestan and Iran cave barbs have differential distribution patterns, with some extent of habitat overlap in the vicinity of the originally discovered cave barb locality. According to the observed distribution patterns, the blind Lorestan cave barb and cave loach are mostly distributed in habitats close to the Seazar River (Dez River drainage, Iran), and the blind Iran cave barb is distributed toward the west and probably in a few springs in the Karkheh River drainage. Our data support the previously proposed distribution pattern for the cave barbs, in which the species show partial niche separation and reproductive isolation, with the Lorestan cave barb being a water flow-dependent species and the Iran cave barb being a generalist species preferring variable flow rates. We showed eDNA metabarcoding to be a useful approach for ecological surveys of subterranean fish biodiversity with implications for conservation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".