DOES PLANT SPECIES CO-OCCURRENCE INFLUENCE SOIL MITE DIVERSITY?
Bibliographic record
Abstract
Few studies have considered whether plant taxa can be used as predictors of belowground faunal diversity in natural ecosystems. We examined soil mite (Acari) diversity beneath six grass species at the Konza Prairie Biological Station, Kansas, USA. We tested the hypotheses that soil mite species richness, abundance, and taxonomic diversity are greater (1) beneath grasses in dicultures (different species) compared to monocultures (same species), (2) beneath grasses of higher resource quality (lower C:N) compared to lower resource quality, and (3) beneath heterogeneous mixes of grasses (C3 and C4 grasses growing together) compared to homogeneous mixes (C3 or C4 grasses) using natural occurrences of plant species as treatments. This study is the first to examine the interaction between above- and belowground diversity in a natural setting with species-level resolution of a hyper-diverse taxon. Our results indicate that grasses in diculture supported a more species and phylogenetically rich soil mite fauna than was observed for monocultures and that this relationship was significant at depth but not in the upper soil horizon. We noted that mite species richness was not linearly related to grass species richness, which suggests that simple extrapolations of soil faunal diversity based on plant species inventories may underestimate the richness of associated soil mite communities. The distribution of mite size classes in dicultures was considerably different than those for monocultures. There was no difference in soil mite richness between grass combinations of differing resource quality, or resource heterogeneity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".