Processes driving individual variation in environmental <scp>DNA</scp> deposition rates in <i>Daphnia magna</i>
Bibliographic record
Abstract
Abstract The abundance of environmental DNA (eDNA) in water samples has been proposed as a sensitive, cost‐efficient, and non‐invasive alternative to infer population abundance and biomass, regardless of the acknowledgment that a number of biotic and abiotic factors can lead to substantially varying rates of eDNA deposition among organisms in a population. We tested how metabolic, nutritional, and life history processes shape intraspecific eDNA deposition rates in the freshwater invertebrate Daphnia magna . We extracted water samples from individual D. magna raised in glass vials under a 2 × 2 longitudinal factorial manipulation of temperature and food levels over their entire lifespan, and quantified eDNA daily deposition rates using digital droplet PCR (ddPCR). Analyzed using a hypothesis‐driven nested mixed‐effect modeling framework, we showed that per individual D. magna eDNA deposition rate varied by an order of magnitude over the course of each individual's lifespan due to multiple causes. We identified that large and pregnant D. magna had the highest eDNA deposition rates, particularly under warmer conditions with higher food levels, and thus, should be considered a prime target for field detections. We found that recently deceased individuals could potentially bias eDNA monitoring efforts by releasing a relatively higher amount of eDNA through decomposition. Our work supplies a more nuanced understanding of myriad factors that shape eDNA deposition, suggesting new and more useful ways to interpret eDNA monitoring data. We recommend that future work using eDNA to estimate population abundance or biomass should account for both energetic conditions and the reproductive cycle facing their target organism and prioritize sampling effort toward metabolically active individuals, especially when working with size‐structured populations that exhibit wide variation in body mass.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".