Spatial scale and nested patterns of beta‐diversity in temperate forest Diptera
Bibliographic record
Abstract
Abstract. 1. We examined whether local assemblages of temperate forest Diptera are structured or simply random sets of species, and at what scale any patterns become apparent. Nested patterns of α‐, β‐, and γ‐diversity in higher Diptera (Schizophora) were described using additive partitioning, to determine the spatial scale contributing the most to species richness. Patterns were examined for Schizophora, two subordinate taxonomic groups (Calyptratae, Acalyptratae) and common versus rare species. 2. A hierarchically nested design was used with three spatial scales: three sites, four stands per site, six trees per stand. Flies were sampled using trunk traps and flight intercept traps in June‐July 2008 in sugar maple stands in southwestern Quebec forest fragments. 3. Species diversity and composition were non‐random at all scales, and varied across scales and among subgroups. Smaller scales (β 1 : between trees) seem to structure species composition of Schizophora, Calyptratae and Acalyptratae. Common species varied more at finer scales (α 1 : within trees); rare species varied more at large scales (β 3 : between sites). The scale contributing the most to γ‐diversity varied across the groups, but β 1 was the overall trend. 4. Diversity patterns differed from those in other forest arthropod taxa, in which larger scales drive overall patterns. This may be explained by the high ecological diversity in Diptera, in which species occurrence is often dictated by the presence of ephemeral, patchy resources within larger sites. The overall similarity from site to site is difficult to explain without genetic evidence as to the extent of dispersal of Diptera between sites.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".