Metabarcoding unsorted kick-samples facilitates macroinvertebrate-based biomonitoring with increased taxonomic resolution, while outperforming environmental DNA
Bibliographic record
Abstract
Abstract Many studies have highlighted the potential of DNA-based methods for the biomonitoring of freshwater macroinvertebrates, however only a few studies have investigated homogenisation of bulk samples that include debris to reduce sample-processing time. In order to explore the use of DNA-based methods in water quality assessment in South Africa, this study compares morphological and molecular-based identification of freshwater macroinvertebrates at the mixed higher taxon and mOTU level while investigating abundance and comparing mOTU recovery with historical species records. From seven sites across three rivers in South Africa, we collected a biomonitoring sample, an intensive-search comprehensive sample and an eDNA sample per site. The biomonitoring sample was picked and scored according to standard protocols and the leftover debris and comprehensive samples were homogenised including all debris. DNA-based methods recovered higher diversity than morphology, but did not always recover the same taxa, even at the family level. Regardless of the differences in taxon scores, most DNA-based methods except some eDNA samples, returned the same water quality assessment category as the standard morphology-based assessment. Homogenised comprehensive samples recovered more freshwater invertebrate diversity than all other methods. The eDNA samples recovered 2 to 10 times more mOTUs than any other method, however 90% of reads were non-target and as a result eDNA recovered the lowest target diversity. However, eDNA did find some target taxa that the other methods failed to detect. This study shows that unsorted samples recover the same water quality scores as a morphology-based assessment and much higher diversity scores than both picked and eDNA samples. As a result, there is potential to integrate DNA-based approaches into existing metrics quickly while providing much more information for the development of more refined metrics at the species or mOTU level with distributional data which can be used for conservation and biodiversity management.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".