Environmental DNA of preservative ethanol performed better than water samples in detecting macroinvertebrate diversity using metabarcoding
Bibliographic record
Abstract
Abstract Aim High‐throughput pipelines supported by eDNA metabarcoding have been applied in various freshwater ecosystems. Both eDNA in ethanol (EtOH) samples (ES‐eDNA) and in water samples (WS‐eDNA) can provide comprehensive classification lists with good taxonomic resolution and coverage for determining freshwater biodiversity and biomonitoring. But, the advantages of ES‐eDNA metabarcoding over WS‐eDNA metabarcoding remain unclear for routine assessments of diversity of benthic macroinvertebrates in streams. Location Qiantang River Basin, China. Methods Here, we compared ES‐eDNA and WS‐eDNA metabarcoding to evaluate the performance of two eDNA workflows in determining biodiversity and recovery of damaged macroinvertebrate communities. All eDNA samples from the environment and bulk specimen of macroinvertebrates were processed into available molecular operational taxonomic units (MOTUs) and identified to the level of genus. Results WS‐eDNA detected more exact sequence variants (ESVs) (formerly referred to as operational taxonomic units; OTUs), than did ES‐eDNA (2,866 vs. 2,406), but fewer macroinvertebrate ESVs (381 vs. 481). Among sampling sites, the two eDNA workflows exhibited relatively large dissimilarity on inferred community composition ( p < .001). Furthermore, ES‐eDNA metabarcoding exhibited more consistent with morphological identification approaches than did WS‐eDNA metabarcoding (24.24% vs. 17.63%, p = .002), especially for species identified by traditional morphology (morphotaxa). Main conclusions Based on the attributes of ES‐eDNA and WS‐eDNA, it is suggested that ES‐eDNA metabarcoding performs better than does WS‐eDNA metabarcoding in detecting local biodiversity and was consistent with morphological results, while WS‐eDNA was more suitable for exploring biodiversity patterns on a broad scale, as it is the easiest and most convenient way to collect samples. Results of this study suggest ES‐eDNA metabarcoding could be an option in building molecular measurement biomonitoring programme based on EtOH sample used for preserving biological samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".