How much is enough? Examining the sampling effort necessary to estimate mean <scp>eDNA</scp> concentrations in lentic systems
Bibliographic record
Abstract
Abstract The concentration of eDNA in an environment can provide important ecological information of relevance for management and conservation, but little research has explored optimizing sampling strategies to estimate mean eDNA concentrations in natural environments. Inter‐replicate eDNA concentrations often exhibit right‐skewed “clustered” or “clumped” distributions, likely due to the stochastic capture of large “aggregate” particles with high eDNA copy numbers. This has important potential implications for modeling the resulting sampling effort necessary to accurately quantify eDNA concentrations. In a previous study, 17–20 Brook Charr eDNA samples were collected from 28 lakes in Québec, Canada. We explored how variation in eDNA concentrations within a lake was affected by several habitat characteristics. We then conducted a power analysis to determine the sampling effort (“minimum n”) necessary to accurately quantify mean lake eDNA concentrations and, using simulations, explored how a bimodal distribution of eDNA particle copy count could affect inter‐replicate variability. The median sample size such that 90% of sample mean estimates were within 20% of the “true” mean was 12.5; a sample size of 20 was sufficient to quantify mean concentrations in 21/28 lakes. We found no evidence that temperature or lake size impacted sample variability. We also found that variance among replicates was non‐linearly related to mean lake eDNA concentration across years: variability was lowest at low and high concentrations and highest at intermediate concentrations. We hypothesize that this resulted from the stochastic capture of large “aggregate” particles at intermediate concentrations; at low concentrations, aggregates were likely rarely captured and at high concentrations may represent a consistent component of total eDNA. Simulations demonstrated that these patterns can emerge from some bimodal eDNA particle “size” distributions. Overall, we conclude that sampling efforts in many previous studies (notably including the authors' own) were potentially low, emphasizing the need to increase spatial replication in lentic surveys.
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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 | 0.010 | 0.029 |
| 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 | 0.002 |
| 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".