A strong relationship between environmental DNA metabarcoding and rank-based abundance of fish
Bibliographic record
Abstract
Abstract Increasingly, molecular methods of species monitoring are integrated into freshwater biodiversity surveys and fisheries management. Inferring organism abundance or biomass from sequence counts derived from metabarcoding data has been an exciting but contentious concept in the biomonitoring community for some time. Although demonstrating a strong correlation with abundance has proven difficult, many researchers have assumed that quantitative metabarcoding data can at least provide broad-scale ranking of abundance. However, robust field validations of this widely-held assumption remain scarce. Here, we analyse metabarcoding read counts of fish eDNA data derived from 20 lakes and use betabinomial mixed effects models to compare this to rank abundance generated from long-term fish survey data. Rank abundance data for 18 species was generated within-species across-sites, meaning that ranks compare the abundance of the same species in different lakes. We also investigated a possible allometric effect on eDNA production by analysing a subset of data for effects of fish body mass on the amount of eDNA sequences. We found a good relationship between species-specific eDNA sequences and within-species rank abundance categories for fishes, with rare fish producing 3% of sequences in a library, moderately abundant producing 7% and abundant fish producing 29%, according to model predictions. We found a small negative effect of body mass on the amount of eDNA sequences, where the proportion of reads recovered significantly decreased with increased mean body mass of the population. Synthesis and applications: The benefit of this approach is the potential for rapid assessment of rank abundance for multiple species, including smaller species which are often missed by conventional methods such as gillnetting, with relatively low amounts of additional effort. This approach will assist practitioners taking a species-based approach to freshwater habitat management in lakes worldwide.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".