A nitrifier‐enriched microbial community contributes to the degradation of environmental <scp>DNA</scp>
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) surveys are a promising alternative to traditional monitoring of invasive species, rare species, and biodiversity. Detecting organism‐specific eDNA reduces the need to collect physical specimens for population estimates, and the high sensitivity of eDNA assays may improve detection of rare or cryptic species. However, correlating estimated concentrations of eDNA with species abundance can be difficult due to the many abiotic and biotic factors that influence eDNA persistence and degradation. Here, we assessed the impact of a nitrifier‐enriched microbial (NEM) community on the persistence and degradation of Hypophthalmichthys molitrix (silver carp) milt eDNA using experimental aquatic mesocosms and a quantitative PCR approach. The NEM community was cultured from combined sediment and water samples collected from a golf course pond in Columbia, Missouri (USA), and experiments were conducted in the dark at 22°C. We found that the NEM community transformed organic nitrogen from silver carp milt to measurable amounts of nitrate, both in the presence and absence of ammonia nitrogen. Additionally, regardless of ammonia availability, milt eDNA followed a one‐phase exponential decay pattern after an initial 24‐h plateau in the presence of the NEM community. However, milt eDNA had a shorter half‐life (12.5 h) in the absence of exogenous ammonia compared to when ammonia was present (15 h). In sterile mesocosms, eDNA was stable during the 72‐h experiment. Together, these results suggest that the presence of microorganisms is necessary for short‐term degradation of eDNA. Furthermore, nitrifying microbial communities, which are ubiquitous in most soil and water environments, could limit eDNA persistence in the environment. Understanding the contributions of environmental microbial communities will allow more confidence in sampling design and eDNA result interpretations for biodiversity management applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.010 |
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".