Research Trends and Hotspots in eDNA-Based Surveys of Macroinvertebrates: A Bibliometric Analysis
Bibliographic record
Abstract
Environmental DNA metabarcoding offers an efficient and non-invasive solution for aquatic biomonitoring surveys, particularly demonstrating remarkable potential in macroinvertebrate research. In this study, we systematically analyzed 322 articles in the Web of Science Core Collection from 2010 to 2024 using bibliometric methods to reveal the research trends and technological advances in the field of macroinvertebrate surveys based on eDNA metabarcoding technology. The results showed that the number of annual publications has increased rapidly since 2012, with the United States (n = 58), France (n = 43), and Canada (n = 39) as the main contributing countries, and the most intensive collaboration network was observed among Europe countries. A keyword analysis shows that, in the past five years, the research direction has shifted to novel methodologies including multi-marker approaches, quantitative PCR, digital PCR, and other molecular detection platforms and high-throughput sequencing technology, with the integration of machine-learning and deep-learning architecture significantly improving the taxonomic resolution of data analysis. Despite the advantages of eDNA metabarcoding technology in terms of sensitivity and efficiency, persistent challenges including false positives/negatives in detection and incomplete reference databases are still the main challenges. This study provides methodological evidence for standardizing eDNA protocols in benthic assessments while identifying critical knowledge gaps.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.112 | 0.124 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".