Detection differences between eDNA and mid‐water trawls are driven by fish biomass and habitat preferences
Bibliographic record
Abstract
Abstract Marine scientific trawl surveys are commonly used to assess the distribution and population size of fisheries‐related species, yet the method is effort‐intensive and can be environmentally destructive. Sequencing environmental DNA (eDNA) from water samples can reveal the presence of organisms in a community without capturing them; however, we expect the detectability of taxa to differ between eDNA and trawl surveys, and understanding how species traits and population variables contribute to detection differences can help calibrate our expectations from each form of sampling. Here, we coupled eDNA metabarcoding and capture trawl surveys in British Columbia, Canada, to examine species traits that explain recurrent differences in detectability between the two methods, including habitat, body size, and biomass. At the regional scale, 17 of 23 fish species (74%) captured by the trawl were detected by eDNA metabarcoding, and 39 additional species were detected by eDNA sampling only. We found that eDNA metabarcoding disproportionately detected trawl‐caught species with greater local biomass (i.e., greater biomass in the adjacent trawl). Fish detected only in eDNA had a greater range of body lengths and a broader range of habitat preferences outside the trawls' target size and sampling areas. Our results suggest that with our level of sampling, eDNA metabarcoding can adequately recapitulate detection of fish communities detected by trawl surveys, but with a bias toward fish of high population biomass and greater inclusion of fish from outside the trawled area.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".