Assessing biodiversity of a freshwater benthic macroinvertebrate community through non-destructive environmental barcoding of DNA from preservative ethanol
Bibliographic record
Abstract
BACKGROUND: Characterizing biodiversity in a habitat or in targeted taxonomically or socioeconomically important groups remains a challenge. Standard DNA-based biodiversity identification tools such as DNA barcoding coupled with high-throughput Next-Generation Sequencing (NGS) technologies are rapidly changing the landscape of biodiversity analysis by targeting various habitats and a wide array of organisms. However, effective use of these technological advances requires optimized protocols and benchmarking against traditional tools. Here we investigate the use of commonly used preservative ethanol as a non-destructive and inexpensive source of DNA for NGS biodiversity analysis of benthic macroinvertebrates. We used the preservative ethanol added to field collected organisms (live sorted bulk benthic samples) as a source of community DNA for NGS environmental barcoding. We directly compare this approach with a DNA barcode library generated using Sanger sequencing of all individuals separated from abenthic sample as well as with NGS environmental barcoding of DNA extracted from mixed/homogenized tissue specimens of the same benthic sample. We also evaluate a multiplex PCR strategy, as compared to commonly used single amplicon workflow, using three newly designed primer sets targeting a wide array of benthic macroinvertebrate taxa. RESULTS: Our results indicate the effectiveness of ethanol-based DNA in providing sequence information from 87% of taxa identified individually from mixture as compared to 89% in conventional tissue extracted DNA. Missing taxa in both DNA sources were from species with the lowest abundance (e.g. 1 individual) in the benthic mixture. Interestingly, we achieved 100% detection for taxa represented with more than 1% individuals in the mixture in both sources of DNA. Our multiplex amplification regime increased the detection as compared to any single primer set indicating the usefulness of using multiple primer sets in initial amplification of target genes. CONCLUSIONS: Although NGS approaches have significantly increased the potential of using DNA information in biodiversity analysis, robust methods are needed to provide reliable data and alleviate sample-processing bottlenecks. Here we coupled non-destructive DNA access and a multiplex PCR approach in NGS environmental barcoding for effective data generation from benthic live-sorted samples collected in bulk and preserved in ethanol. Our study provides a possible solution to sampling and vouchering challenges in using benthic samples through next-generation environmental barcoding and facilitates wider utility of DNA information, especially species-specific DNA barcodes, in ecological and environmental studies and real-world applications such as biomonitoring programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".