Utilizing aquatic environmental DNA to address global biodiversity targets
Bibliographic record
Abstract
Achieving global biodiversity goals requires assessing, attributing and reversing the ongoing, unprecedented biodiversity decline in aquatic ecosystems, and relies on adequate data to inform policy and action. Analysis of environmental DNA (eDNA) has become established as a novel and powerful approach to assess the state and functioning of aquatic ecosystems, and although increasingly implemented by stakeholders its potential is not yet fully tapped. In this Perspective, we review the current state of aquatic eDNA research, focusing in particular on the policy relevance of eDNA and its utility in contributing towards the Kunming–Montreal Global Biodiversity Framework. We summarize key technological developments in eDNA science to measure organismal diversity, its potential for spatial and temporal upscaling to become a key reference for local to global biodiversity action, and the next steps needed to effectively implement eDNA for decision-making and reaching biodiversity targets. Using eDNA to support biodiversity assessment will particularly benefit the understanding of understudied ecosystems and allow the direct calculation of ecological indices and implementation of FAIR (findable, accessible, interoperable and reusable) and inclusive data curation. Important next steps for eDNA require proper method standardization and commonly agreed quality standards, populating reference databases, and overcoming methodological constraints in retrofitting novel eDNA-based approaches to existing biodiversity monitoring approaches. Aquatic eDNA-based technologies offer the potential for universal and standardized biodiversity monitoring. In this Perspective, Altermatt et al. discuss how these technologies can help to achieve the targets of the Kunming–Montreal Global Biodiversity Framework through informing appropriate policy and actions, and describe the next steps required for widespread and equitable use of these technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".