eDNA in subterranean ecosystems: Applications, technical aspects, and future prospects
Bibliographic record
Abstract
Monitoring of biota is pivotal for the assessment and conservation of ecosystems. Environments worldwide are being continuously and increasingly exposed to multiple adverse impacts, and the accuracy and reliability of the biomonitoring tools that can be employed shape not only the present, but more importantly, the future of entire habitats. The analysis of environmental DNA (eDNA) metabarcoding data provides a quick, affordable, and reliable molecular approach for biodiversity assessments. However, while extensively employed in aquatic and terrestrial surface environments, eDNA-based studies targeting subterranean ecosystems are still uncommon due to the lack of accessibility and the cryptic nature of these environments and their species. Recent advances in genetic and genomic analyses have established a promising framework for shedding new light on subterranean biodiversity and ecology. To address current knowledge and the future use of eDNA methods in groundwaters and caves, this review explores conceptual and technical aspects of the application and its potential in subterranean systems. We briefly introduce subterranean biota and describe the most used traditional sampling techniques. Next, eDNA characteristics, application, and limitations in the subsurface environment are outlined. Last, we provide suggestions on how to overcome caveats and delineate some of the research avenues that will likely shape this field in the near future. We advocate that eDNA analyses, when carefully conducted and ideally combined with conventional sampling techniques, will substantially increase understanding and enable crucial expansion of subterranean community characterisation. Given the importance of groundwater and cave ecosystems for nature and humans, eDNA can bring to the surface essential insights, such as study of ecosystem assemblages and rare species detection, which are critical for the preservation of life below, as well as above, the ground.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".