Spatial variance in soil microarthropod communities: Niche, neutrality, or stochasticity?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it