Spatial variance in soil microarthropod communities: Niche, neutrality, or stochasticity?
Bibliographic record
Abstract
We studied geographic patterns in the soil microarthropods associated with moss carpets on exposed rocky outcrops in southwestern British Columbia, Canada. We related microarthropod composition, abundance, and species richness to 15 ecological variables relevant to either spatial or environmental filtering. Our survey identified 352 morphospecies in 32 sites spanning a 130- × 60-km area. We tested whether the relative importance of spatial and environmental factors was concordant between community composition, abundance, species richness, and 3 major taxonomic groups (Oribatida, Mesostigmata, Collembola). The results depended on the variance partitioning methods used and whether composition was defined by species abundance or presence. Distance-based Mantel tests showed that dissimilarity in species composition between sites was better predicted by spatial distance than by environmental dissimilarity. In contrast, variance partitioning of ordinated abundance data concluded that environmental rather than spatial variables explained most variance in the composition of total microarthropod, especially Collembola, assemblages. Total abundance and species richness were only weakly correlated across space, even though both were explained by environmental factors such as temperature and soil moisture. Given the surprising contradictions between methods, we suggest that different analyses should always be compared to fully uncover the spatial and environmental factors structuring communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".