Potential of environmental DNA for tracing land-use based sediment sources
Bibliographic record
Abstract
Environmental DNA (eDNA) is a complex mixture of genetic material extracted from environmental samples like soil, water or sediment in order to obtain reliable information on the past and current biological communities. In recent years, the eDNA technique was successfully applied to sediment accumulated in lakes for providing information on past land use and land cover changes in their drainage areas. Recently, the potential of eDNA for providing detailed information on the plant species found in sediment sources was investigated. These research highlight the powerful potential of this method for improving our ability to detect the vegetal communities causing erosion and sediment delivery. Nevertheless, some fundamental questions remain like for example the DNA memory effect of soils. How long the plant signature can persist in soils? Are we recording the last species cultivated or a mixture of past plants in agricultural areas? These issues are of prime importance for examining the potential of eDNA as a new sediment tracer. To answer these questions, two contrasted sites located in intensively cultivated environments in France were studied. In the first site, soil samples were collected (n=30) in plots for which the crop rotation history was well documented since 1975. In particular, crops cultivated only once during the rotation were used as potential chronological markers. The impact of agricultural practices on eDNA preservation was also investigated comparing soil signatures under conventional and conservation farming. In the second site, samples were collected (n=40) to compare the abundance of currently observed taxa versus detected taxa in cropland, grassland, woodland and river channel banks. The results showed that the last cultivated crop was detected in 100% of the samples as the most abundant taxa under conventional farming and 75% under no-tillage. The last cultivated species was the most abundant in 80% of the studied plots. Interestingly, grapevine was detected in 46% of the cultivated plots of the second site, although this plant is no longer cultivated in this catchment. In addition, a large variety of weeds were detected in both sites in addition to the cultivated species. eDNA results provided by the current research illustrate the potential of this method for identifying the recent (<7 years) land cover history of soils which may allow to improve our understanding of sediment mobilization and transfer processes over short timescales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".