Exploring the influence of natural features on soil mesofaunal communities in agricultural landscapes through DNA metabarcoding
Bibliographic record
Abstract
Soil mesofauna critically support organic matter decomposition and nutrient cycling, as well as regulating pests and diseases. Agricultural activities can physically and chemically influence soil mesofauna communities, thereby impacting the ecosystem services they provide. Conserving natural features in agricultural environments is well known to support biodiversity and associated ecosystem services, yet soil mesofauna communities and their functions in such features require further study. We sampled the soil mesofaunal communities (focusing on Acari [mites] and Collembola [springtails]) in agriculturally-dominated landscapes in southeastern Ontario, Canada. Semi-natural features were represented by drainage ditch banks with varying amounts of woody vegetation to different kinds of forested blocks. Specimens were extracted using Berlese funnels, and COI metabarcoding was conducted on the bulk specimen samples. A total of 585 operational taxonomic units (OTUs; a proxy for species) were identifiable based on DNA sequences, 390 of which belonged to either Acari (273) or Collembola (117); however, it was rarely possible to identify specimens to species-level. Mesofaunal richness did not differ among habitat types but community composition varied between low woody-vegetation drainage bank sites and forested blocks. Maintaining heterogeneous natural and semi-natural features appears to increase the overall diversity of mesofauna on the landscape, and even small forest patches support forest-associated soil biodiversity in this agricultural region.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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".