Effect of environmental DNA sampling resolution compared to capture surveys in detecting marine biodiversity
Bibliographic record
Abstract
Marine environmental DNA (eDNA) metabarcoding provides a tool that can increase our ability to monitor biodiversity, but the effects of seawater movement and mixing on the spatial resolution of eDNA needs to be better understood. Specifically, homogenization of eDNA could limit or alter our ability to infer biodiversity patterns and species distributions, especially at fine scales. We paired eDNA metabarcoding and beach seining surveys of fishes on the Pacific coast of British Columbia to assess how conditions at differing spatial scales influence eDNA spatial turnover and richness. Between paired eDNA and beach seine samples, more fish taxa were detected in eDNA, especially in areas of high seawater movement and high habitat heterogeneity, but eDNA consistently missed species that were present in low biomass in beach-seining surveys. Spatial turnover of fish communities surveyed using beach seining was explained by factors that vary at smaller spatial scales than with eDNA. Specifically, differences in vegetation (varying at 10s -100s of meters) and shoreline exposure (100s - 1000s of meters) explained community turnover in beach seining samples, and there was no additional effect of distance between samples, suggesting similar habitats far apart had similar fish communities. Conversely, turnover in eDNA was not well explained by small-scale habitat variation (vegetation) but showed a pattern of distance decay, suggesting habitat features at larger scales drive community patterns. In eDNA, samples within two km had similar compositions (<10 % turnover) which dissipated by ~10 km. Our findings indicate that in heterogeneous coastal seascapes, the eDNA sample grain is larger than smaller-scale (and potentially important) differences in fish distributions captured with beach seining. Yet eDNA sampling can provide a powerful and realistic summary of fish biodiversity over larger areas at the scales, potentially relevant to observing regional biodiversity change
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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 | 0.040 | 0.084 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".