Effect of environmental DNA sampling resolution in detecting nearshore fish biodiversity compared to capture surveys
Bibliographic record
Abstract
Sampling and sequencing marine environmental DNA (eDNA) provides a tool that can increase our ability to monitor biodiversity, but movement and mixing of eDNA after release from organisms before collection could affect our inference of species distributions. To assess how conditions at differing spatial scales influence the inferred species richness and compositional turnover, we conducted a paired eDNA metabarcoding and capture (beach seining) survey of fishes on the coast of British Columbia. We found more taxa were typically detected using eDNA compared to beach seining. eDNA identified more taxa with alternative habitat preferences, and this richness difference was greater in areas of high seawater movement, suggesting eDNA has a larger spatial grain influenced by water motion. By contrast, we found that eDNA consistently missed low biomass species present in seining surveys. Spatial turnover of communities surveyed using beach seining differed from that of the eDNA and was better explained by factors that vary at small (10-1000s meters) spatial scales. Specifically, vegetation cover and shoreline exposure explained most species turnover from seining, while eDNA turnover was not explained by those factors and showed a distance decay pattern (a change from 10% to 25% similarity from 2 km to 10 km of distance), suggesting unmeasured environmental variation at larger scales drives its turnover. Our findings indicate that the eDNA sample grain is larger than that of capture surveys. Whereas seining can detect differences in fish distributions at scales of 10s-100s of meters, eDNA can best summarize fish biodiversity at larger scales possibly more relevant to regional biodiversity assessments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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 | 0.038 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".