How <scp>eDNA</scp> data filtration, sequence coverage, and primer selection influence assessment of fish communities in northern temperate lakes
Bibliographic record
Abstract
Abstract For nearly 15 years now, environmental DNA has demonstrated its effectiveness in monitoring biodiversity. Methodological and technical improvements have significantly enhanced the field. However, the effect of factors such as sequence coverage, bioinformatic filtration, and primer choice have been less explored or need to be optimized according to specific survey objectives and study site characteristics. We evaluated these factors to help optimize monitoring fish biodiversity in North American temperate lakes. We sampled water for fish community eDNA analysis in 12 lakes from southwestern Québec, Canada. The lakes were selected to encompass a wide range of surface areas and species richness. We sampled water from a total of 520 sites (25–50 per lake) and analyzed three mitochondrial DNA regions (12S rRNA; 16S rRNA; and cytb) using NovaSeq sequencing. Our results, based on rarefied count matrices (from a sequencing depth of 100,000 to a minimum depth of 1000 reads per sample), showed that keeping only species in each sample if they represented at least one thousandth (species minimum read proportion threshold = 0.001) of the sample's reads was adequate to remove false positives and had a limited negative impact on true positives with low read counts. The sequencing depth was found to have a negligible impact on the accuracy of fish community assessment in a given lake. With the same sequencing depth and a complete local reference database for each primer set, a single primer set produced similar species richness medians than the combination of two or three primer sets. Overall, 12S and 16S detected more species and provided more consistent community profiles than cytb. Based on our observations, we suggest using the 12S MiFish‐U primer set and applying a minimum proportion of 0.001 reads per species and site to monitor north‐temperate lentic freshwater fish communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".