Optimized <scp>DNA</scp> isolation from marine sponges for natural sampler <scp>DNA</scp> metabarcoding
Bibliographic record
Abstract
Abstract Marine sponges have recently been recognized as natural samplers of environmental DNA (eDNA) due to their effective water filtration and their ubiquitous, sessile, and regenerative nature. However, laboratory workflows for metabarcoding of sponge tissue have not been optimized to ensure that these natural samplers achieve their full potential for community survey. We used a phased approach to investigate the influence of DNA isolation procedures on the biodiversity information recovered from sponges. In Phase 1, we compared three treatments of residual ethanol preservative in sponge tissue alongside five DNA extraction protocols. The results of Phase 1 informed which ethanol treatment and DNA extraction protocol should be used in Phase 2, where we assessed the effect of starting tissue mass on extraction success and whether homogenization of sponge tissue is required. Phase 1 results indicated that ethanol preservative may contain unique and/or additional biodiversity information to that present in sponge tissue, but blotting tissue dry generally recovered more taxa and generated more sequence reads from the wild sponge species. Tissue extraction protocols performed best in terms of DNA concentration, taxon richness, and proportional read counts, but the non‐commercial tissue protocol was selected for Phase 2 due to cost‐efficiency and greater recovery of target taxa. In Phase 2 overall, we found that homogenization may not be required for sponge tissue and more starting material does not necessarily improve taxon detection. These results combined provide an optimized DNA isolation procedure for sponges to enhance marine biodiversity assessment using natural sampler DNA metabarcoding.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".