Oribatid mite taxa and composition associated with temperate habitats in Great Britain
Bibliographic record
Abstract
Knowledge on the status of soil biodiversity and its variation across habitats is fundamentally important to soil monitoring. Oribatid mites are globally distributed, can be found in all terrestrial ecosystems and, being generally numerous and including various trophic traits, are important components in soil food webs for the ecosystem services they deliver. The Countryside Survey (CS) is an integrated monitoring programme in Great Britain, and here we analyse an existing dataset of oribatid mite records from soil invertebrate assessments of CS in 1998 that covered over 500 one-kilometre squares. Using vegetation-based classification (AVC) to represent broad habitat types, we tested differences in oribatid mite richness and community composition across these, and used indicator analysis to uncover taxa associations with habitats or habitat combinations. Furthermore, we explored links between species and soil properties using richness and prevalence across organic matter and pH gradients. Oribatid mite species richness and composition differed between habitat types. Lowland and Upland wooded habitats had highest species richness per core; richness was lower in the managed agricultural habitats (Crops & Weeds, Tall Grass & Herb, Fertile Grassland) and generally higher in wooded habitats and those typically with organic soils (Lowland Wooded, Upland Wooded, Moorland-Grass mosaic, Heath & Bog). Oribatid mite richness increased steeply to ∼30 % organic matter. We list several species associated with AVCs that can potentially be used as indicators. These findings reinforce the link between oribatid mites, habitat, soil organic matter and pH, and provide a basis for mapping and further analyses.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".