Estimating fish abundance and biomass from <scp>eDNA</scp> concentrations: variability among capture methods and environmental conditions
Bibliographic record
Abstract
Environmental DNA (eDNA) promises to ease noninvasive quantification of fish biomass or abundance, but its integration within conservation and fisheries management is currently limited by a lack of understanding of the influence of eDNA collection method and environmental conditions on eDNA concentrations in water samples. Water temperature is known to influence the metabolism of fish and consequently could strongly affect eDNA release rate. As water temperature varies in temperate regions (both seasonally and geographically), the unknown effect of water temperature on eDNA concentrations poses practical limitations on quantifying fish populations using eDNA from water samples. This study aimed to clarify how water temperature and the eDNA capture method alter the relationships between eDNA concentration and fish abundance/biomass. Water samples (1 L) were collected from 30 aquaria including triplicate of 0, 5, 10, 15 and 20 Brook Charr specimens at two different temperatures (7 °C and 14 °C). Water samples were filtered with five different types of filters. The eDNA concentration obtained by quantitative PCR (qPCR) varied significantly with fish abundance and biomass and types of filters (mixed-design ANOVA, P < 0.001). Results also show that fish released more eDNA in warm water than in cold water and that eDNA concentration better reflects fish abundance/biomass at high temperature. From a technical standpoint, higher levels of eDNA were captured with glass fibre (GF) filters than with mixed cellulose ester (MCE) filters and support the importance of adequate filters to quantify fish abundance based on the eDNA method. This study supports the importance of including water temperature in fish abundance/biomass prediction models based on eDNA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".