Detection of fish sedimentary <scp>DNA</scp> in aquatic systems: A review of methodological challenges and future opportunities
Bibliographic record
Abstract
Abstract Environmental DNA studies have proliferated over the last decade, with promising data describing the diversity of organisms inhabiting aquatic and terrestrial ecosystems. The recovery of DNA present in the sediment of aquatic systems (sedDNA) has provided short‐ and long‐term data on a wide range of biological groups (e.g., photosynthetic organisms, zooplankton species) and has advanced our understanding of how environmental changes have affected aquatic communities. However, substantial challenges remain for recovering the genetic material of macro‐organisms (e.g., fish) from sediments, preventing complete reconstructions of past aquatic ecosystems, and limiting our understanding of historic, higher trophic level interactions. In this review, we outline the biotic and abiotic factors affecting the production, persistence, and transport of fish DNA from the water column to the sediments, and address questions regarding the preservation of fish DNA in sediment. We identify sources of uncertainties around the recovery of fish sedDNA arising during the sedDNA workflow. This includes methodological issues related to experimental design, DNA extraction procedures, and the selected molecular method (quantitative PCR, digital PCR, metabarcoding, metagenomics). By evaluating previous efforts (published and unpublished works) to recover fish sedDNA signals, we provide suggestions for future research and propose troubleshooting workflows for the effective detection and quantification of fish sedDNA. With further research, the use of sedDNA has the potential to be a powerful tool for inferring fish presence over time and reconstructing their population and community dynamics.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".