A systematic review evaluating the performance of eDNA methods relative to conventional methods for biodiversity monitoring
Bibliographic record
Abstract
The rapid adoption of environmental DNA (eDNA) methods has drastically changed biodiversity monitoring efforts. It is often claimed that eDNA methods are more sensitive and efficient than conventional biodiversity monitoring methods, but it is often unclear what metrics support this claim. There have been many direct comparative studies between eDNA and conventional methods; several supporting the increased sensitivity and efficiency of eDNA methods, others finding the opposite. Here, we systematically review all comparative studies between eDNA and conventional methods published between 2008 and 2023. We review various metrics used to evaluate the relative performance of eDNA methods and whether study characteristics influenced comparative outcomes. We found that eDNA is more likely to provide increased estimates of sensitivity metrics (i.e. species richness and detection probability) and lower estimates of efficiency metrics (i.e. cost and sampling time/effort). However, eDNA methods displayed their own biases, often recovering communities distinct from those revealed via conventional methods. While eDNA methods were capable of describing abundance and improving taxonomic resolution, we observed substantial variation. Trends in comparative outcomes were consistent across study characteristics, but we highlight areas that have received little exploration into the relative performance of eDNA, including across much of the Global South and the ability of eDNA to monitor temporal changes in biodiversity. Our review provides a comprehensive examination of eDNA comparative studies and delivers clarity to conservation professionals on where, when, and how eDNA methods are likely to add value to biodiversity monitoring initiatives.
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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 | 0.018 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".