Optimized <scp>DNA</scp> isolation from marine sponges for natural sampler <scp>DNA</scp> metabarcoding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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 it