Evaluation of lake sedimentary ancient <scp>DNA</scp> metabarcoding to assess fungal biodiversity in Arctic paleoecosystems
Bibliographic record
Abstract
Abstract Fungi are crucial organisms in most ecosystems as they exert ecological key functions and are closely associated with land plants. Fungal community changes may, therefore, help reveal biodiversity changes in past ecosystems. Lake sediments contain the DNA of organisms in the catchment area, which allows reconstructing past biodiversity by using metabarcoding of ancient sedimentary DNA. We re‐evaluated various commonly used metabarcoding primers, and we developed a novel PCR primer combination for fungal metabarcoding to produce a short amplicon, thus accounting for amplification bias due to the degradation of ancient DNA. In silico PCRs showed higher diversity using this new primer combination, compared with previously established fungal metabarcoding primers. We analyzed data from sediment cores from four artic and one boreal lake in Siberia. These cores had been stored for 2–22 years after coring; we, therefore, examined the degradation effects of ancient DNA and storage time‐related bias affecting fungal communities. Amplicon lengths showed considerable variation within and between the major divisions of fungi, for example, amplicons of Basidiomycota were significantly longer than those of Mucoromycota; however, we observed no significant effect of sample age on amplicon length and GC content, suggesting the robustness of our results. We also found no indication of post‐coring fungal growth during storage regarding the proportions of common mold taxa, which would otherwise distort conclusions on past fungal communities. Terrestrial soil fungi, including mycorrhizal fungi and saprotrophs, were predominant in all lakes, whereas typical aquatic taxa were only represented to a negligible extent, which supports the use of lake sedimentary ancient DNA for reconstructing terrestrial communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".